The tremendous growth in the transportation sector as a result of changes in our ways of transport and a rise in the level of prosperity was reflected directly by the intensification of energy needs. Thus, electric vehicles (EV) have been produced to minimise the energy consumption of conventional vehicles. Although the EV motor is more efficient than the internal combustion engine, the well to wheel (WTW) efficiency should be investigated in terms of determining the overall energy efficiency. In simple words, this study will try to answer the basic question – is the electric car really energy efficient compared with ICE-powered vehicles? This study investigates the WTW efficiency of conventional internal combustion engine vehicles ICEVs (gasoline, diesel), compressed natural gas vehicles (CNGV) and EVs. The results show that power plant efficiency has a significant consequence on WTW efficiency. The total WTW efficiency of gasoline ICEV ranges between 11–27 %, diesel ICEV ranges from 25 % to 37 % and CNGV ranges from 12 % to 22 %. The EV fed by a natural gas power plant shows the highest WTW efficiency which ranges from 13 % to 31 %. While the EV supplied by coal-fired and diesel power plants have approximately the same WTW efficiency ranging between 13 % to 27 % and 12 % to 25 %, respectively. If renewable energy is used, the losses will drop significantly and the overall efficiency for electric cars will be around 40–70% depending on the source and the location of the renewable energy systems.
This study investigates the best available methods for remote monitoring inland small-scale waterbodies, using remote sensing data from both Landsat-8 and Sentinel-2 satellites, utilizing a handheld hyperspectral device for ground truthing. Monitoring was conducted to evaluate water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and turbidity. Ground truthing was performed to select the most suitable atmospheric correction technique (ACT). Several ACT have been tested: dark spectrum fitting (DSF), dark object subtraction (DOS), atmospheric and topographic correction (ATCOR), and exponential extrapolation (EXP). Classical sampling was conducted first; then, the resulting concentrations were compared to those obtained using remote sensing analysis by the above-mentioned ACT. This research revealed that DOS and DSF achieved the best performance (an advantage ranging between 29% and 47%). Further, we demonstrated the appropriateness of the use of Sentinel-2 red and vegetation red edge reciprocal bands 1 / B 4 × B 6 for estimating Chl-a ( R 2 = 0.82 , RMSE = 14.52 mg / m 3 ). As for Landsat-8, red to near-infrared ratio ( B 4 / B 5 ) produced the best performing model ( R 2 = 0.71 , RMSE = 39.88 mg / m 3 ), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model ( R 2 = 0.85 , RMSE = 0.87 NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with R 2 = 0.64 , RMSE = 0.90 NTU) using Landsat-8 was performed by applying two bands ( B 1 / B 3 ). Mapping the water quality parameters using the best performance biooptical model showed the significant effect of the adjacent land on the boundary pixels compared to pixels of deeper water.
This study focuses on the utilization of multispectral satellite images for remote water-quality evaluation of inland water body in Jordan. The geophysical parameters based on water’s optical properties, due to the presence of optically active constituents, are used to determine contaminant level in water. It has a great potential to be employed for continuous and cost-effective water-quality monitoring and leads to a reliable regularly updated tool for better water sector management. Three sets of water samples were collected from three different dams in Jordan. Chl-a concentration of the water samples was measured and used with corresponding Sentinel 2 surface reflectance (SR) data to develop a predictive model. Chl-a concentrations and corresponding SR data were used to calibrate and validate different models. The predictive capability of each of the investigated models was determined in terms of determination coefficient (R2) and lowest root mean square error (RMSE) values. For the investigated sites, the B3/B2 (green/blue bands) model and the Ln (B3/B2) model showed the best overall predictive capability of all models with the highest R2 and the lowest RMSE values of (0.859, 0.824) and (30.756 mg/m3, 29.787 mg/m3), respectively. The outcome of this study on selected sites can be expanded for future work to cover more sites in the future and ultimately cover all sites in Jordan.
Global endeavors to respond to the problems caused by climate change and are leading to higher temperatures inside homes, which can cause skin conditions (such as eczema), lethargy, and poor concentration; disturbed sleep and fatigue are also rising. The energy performance of buildings is influenced by interactions and associations of numerous different variables, such as the envelope specifications as well as the design, technologies, apparatuses, and occupant behaviours. This paper introduces simple and sustainable strategies that are not dependent on expensive or sophisticated technologies, as they rely only on the actions practiced by the building’s occupants (movable window shading, and nighttime natural ventilation) instead of completely relying on high-cost mechanical cooling systems in buildings located in the main Eastern Mediterranean climates represented in the country of Jordan. These low-energy solutions could be applied to low-income houses in hot areas to avoid health problems, such as dermatological diseases, and save a significant amount of energy. The final results indicate that window shading has significant potential in reducing the cooling load in different climate zones. Natural ventilation exhibits high energy-saving abilities in climates that have cool nights, whereas its abilities in hot climates where nights are moderate is limited.
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