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.

Build-up/wash-off models were originally developed for small-scale laboratory facilities with uniform properties. The effective translation of these models to catchment scale necessitates the meticulous calibration of model parameters. The present study combines the Mat-SWMM tool with a genetic algorithm (GA) to improve the calibration of build-up and wash-off parameters. For this purpose, Mat-SWMM was modified to equip it with the capacity to provide comprehensive water quality analysis outcomes. Additionally, this research also conducts a comparative examination of two distinct types of objective functions in the optimization. Rather than depending on previous literature, this study undertook a numerical campaign to ascertain an appropriate range for the relevant parameters within the case study, thereby ensuring the optimization algorithm's efficient functionality. This research also implements an integrated event calibration approach, i.e., a novel method that calibrates all rainfall events collectively, thus improving systemic interaction representation and model robustness. The findings indicate that employing this methodology significantly enhances the reliability of the outcomes, thereby establishing a more robust procedure. The first objective function (TSS instantaneous less squared difference function, OF 1), which is widely employed in the literature, was designed to minimize the difference between observed and predicted instantaneous Total Suspended Solids (TSS) concentrations. In contrast, the second function (mass and mass peak consistency function, OF 2) considers integral model outputs, i.e., the overall mass balance, the time of the peak mass flow rate, and its intensity. The analysis of the outputs revealed that both objective functions demonstrated sufficient performance. OF 1 provided slightly better performance in predicting the TSS concentrations, whereas OF 2 demonstrated superior ability in capturing global event characteristics. Notably, the optimal parameter set identified through OF 2 aligned with the physically plausible ranges traditionally recommended in technical manuals for urban catchments. In contrast, OF 1's optimal set necessitated an expansion in the acceptable parameter ranges. Finally, from a computational burden viewpoint, OF 1 demanded a significantly higher number of function evaluations, thus implying an escalating computational cost as the range expands. Conversely, OF 2 necessitated fewer evaluations to converge toward the optimal solution.

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 for estimating Chl-a ( , ). As for Landsat-8, red to near-infrared ratio ( ) produced the best performing model ( , ), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model ( , NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with