We describe a unique, one year investigation of CO 2 and N 2 O fluxes over a fertilized grassland in Ireland using two eddy covariance systems. As the global warming potential (GWP) of N 2 O is 296 (100 year time horizon), relatively small N 2 O emissions have a potentially large impact on overall radiative forcing. Therefore nitrogen fertilizer application practices may possibly turn a site with a net CO 2 uptake into a net radiative forcing source. We observed a net annual uptake of 9.45 T CO 2 ha −1 . N 2 O emissions equivalent to 5.42 T ha −1 CO 2 GWP counteracted 57% of the effect of the CO 2 uptake. Estimated methane emissions from ruminants (3.74 T ha −1 CO 2 GWP) further counteract the CO 2 uptake, making the overall GWP nearly neutral. This delicate balance of the greenhouse gas fluxes underscores the significance of fertilizer application strategies in determining whether a managed grassland is a net GWP source or sink.
In recent years, the sustainability of wind power has been called into question because there are currently no truly sustainable solutions to the problem of how to deal with the non-biodegradable fibre-reinforced polymer (FRP) composite wind blades (sometimes referred to as “wings”) that capture the wind energy. The vast majority of wind blades that have reached their end-of-life (EOL) currently end up in landfills (either in full-sized pieces or pulverized into smaller pieces) or are incinerated. The problem has come to a head in recent years since many countries (especially in the EU) have outlawed, or expect to outlaw in the near future, one or both of these unsustainable and polluting disposal methods. An increasing number of studies have addressed the issue of EOL blade “waste”; however, these studies are generally of little use since they make predictions that do not account for the manner in which wind blades are decommissioned (from the time the decision is made to retire a turbine (or a wind farm) to the eventual disposal or recycling of all of its components). This review attempts to lay the groundwork for a better understanding of the decommissioning process by defining how the different EOL solutions to the problem of the blade “waste” do or do not lead to “sustainable decommissioning”. The hope is that by better defining the different EOL solutions and their decommissioning pathways, a more rigorous research base for future studies of the wind blade EOL problem will be possible. This paper reviews the prior studies on wind blade EOL and divides them into a number of categories depending on the focus that the original authors chose for their EOL assessment. This paper also reviews the different methods chosen by researchers to predict the quantities of future blade waste and shows that depending on the choice of method, predictions can be different by orders of magnitude, which is not good as this can be exploited by unscrupulous parties. The paper then reviews what different researchers define as the “recycling” of wind blades and shows that depending on the definition, the percentage of how much material is actually recycled is vastly different, which is also not good and can be exploited by unscrupulous parties. Finally, using very recent proprietary data (December 2022), the paper illustrates how the different definitions and methods affect predictions on global EOL quantities and recycling rates.
This paper describes a refinement of wind speed prediction methods in order to enhance their accuracy for wind energy applications. Specifically, techniques used to downscale raw forecasts from numerical weather prediction models are investigated. Many downscaling techniques have been proposed, however most of these rely on wind speed data while ignoring a potentially valuable source of information, namely wind direction. In this paper, we incorporate wind speed and direction into three downscaling methods: linear model output statistics; feedforward artificial neural network (ANN); and Kalman filter (KF). We apply the techniques to downscale outputs of a global numerical weather prediction model to six test locations in Ireland for which wind speed and direction measurements were available. While classical downscaling methods require large sets of historical data in order to be trained, the KF has the potential to rapidly estimate the bias that needs to be added to the raw forecasts in order to provide the best fit possible to local observations. Comparing the results of the three downscaling methods, it is shown that while the levels of prediction accuracy attainable with the KF are similar to classical techniques, the amount of data required to parameterise the KF is much less than for other techniques. The KF has a further advantage over the ANN in that it does not require offline parameterisation. However, in this study, the ANN performance was more satisfactory in reducing prediction errors.
Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland ================================= README version 1.3, 21/07/2022 ================================== Contact info: Paul Leahy, University College Cork paul.leahy@ucc.ie | +353 21 4902017 ================================ Contents1. Measurement location and time period2. What is measured (brief description)3. Instrumentation4. CSV file detailed descriptions ================================ 1. Measurement location and time period: North roof of Kane Building, University College Cork (UCC), Ireland. Lat 51 d 53 m 34 s N. Long 8 d 29 m 39 s W. Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building). The measurements were taken over a time period of several months in the years 2013 / 2014. ================================= 2. What is measured (brief description): * LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level. * Air temperature, atmospheric pressure, relative humidity. * Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level). * 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided. See 'CSV file detailed description' below for detailed information. * Diagnostic information. ================================= 2.1 Surrounding terrain: Surrounding area is urban/suburban. The aspect is northerly. To the West: 2-5 storey buildings, open spaces, suburban. To the South: 2-3 storey buildings, open spaces, trees, river. To the East: 2-3 storey buildings, open spaces. To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban. ================================= 3. Instrumentation: ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS. ================================= 4. CSV files detailed description:4.1 Data on 10-minute averages: Filename 05092013-03122013_10min_res.csv contains: 10 minute averaged data from 05/09/2013 to 03/12/2013. Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level. Filename 03122013-07082014_10min_res.csv contains: 10 minute averaged data from: 03/12/2013 to 07/08/2014. Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level. Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues). The first two rows contain header information. Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds. Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West. Row 2 contains the data column headers including units. Wind speeds at each altitude are recorded: No of Packets (= number of scan units averaged over) [] Wind direction (mean) [deg] Horizontal wind speed (mean) & standard deviation [m/s] Vertical wind speed (mean) & standard deviation [m/s] Horizontal variance [m^2/s^2] Horizontal min [m/s] Horizontal max [m/s] TI (turbulence intensity) [] Other meteorological data: Air temperature [oC] Pressure [mbar] Rel. Humidity [%] Rain indicator [unitless] Higher values indicate more rain during the averaging interval. Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer) Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer). Other housekeeping and diagnostic data: Instrument tilt [deg] Instrument bearing [deg] GPS data [degrees N, degrees W] Battery voltage [V] Optics, electronics and battery temperature [oC] ===================================================== 4.2 Data with high time resolution (~23 s): Filename 05092013-11112013_23s_res.csv contains: High resolution data from 05/09/2013 to 11/11/2013 Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m. Note on time resolution: The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete. The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values. Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds. Row 2 contains the data column headers including units. Wind speeds at each altitude are recorded: No of Packets (= scan units averaged over) [] Wind direction (mean) [deg] Horizontal wind speed (mean) & standard deviation [m/s] Vertical wind speed (mean) & standard deviation [m/s] Horizontal variance [m^2/s^2] not defined as measurement interval is too short. Horizontal min [m/s] not defined as measurement interval is too short. Horizontal max [m/s] not defined as measurement interval is too short. TI (turbulence intensity) [] not defined as measurement interval is too short. Other meteorological data: Air temperature [oC] Pressure [mbar] Rel. Humidity [%] Rain indicator [unitless] Higher values indicate more rain during the scanning interval. Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer) Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer. Other housekeeping and diagnostic data: Instrument tilt [deg] Instrument bearing [deg] GPS data [degrees N, degrees W] Battery voltage [V] Optics, electronics and battery temperature [oC] ===================================================== 4.3 Quality control indicators: 9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog 9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path Status Flag 'Green' => good =======================================================
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