Wind energy is one of the most cost-effective forms of renewable energy source with significant increment in yearly installed capacities all around the world. In this study, three commercial wind turbines, namely POLARIS P15-50, POLA-RIS P50-500 and VESTAS V110-2.0, were chosen as large-scale wind energy conversion systems (WECSs) for technical assessment of electric power generation in eight selected locations spreading across the Tigray region of Ethiopia. The economic evaluations of these three WECSs for electric power generation in the selected locations were estimated using present value of cost (PVC) method. These results showed that the highest capacity factor is obtained as 7.873 % using VESTAS V110-2.0 at Mekele, while the lowest as 0.002 % using POLARIS P15-50 at Shire. Average minimum cost per kWh obtained at Mekele was 0.0011$/kWh using VESTAS V110-2.0, while the highest average cost was 7.3148$/kWh using POLARIS P15-50 at Shire. Furthermore, it can be suggested that Atsbi, Chercher, Mekele and Senkata were most profitable for electrical and mechanical applications than hydropower cost in the country.

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The predominant air pollutants in urban cities are (NOx = (NO + NO2), O3 and (OX = (O3 + NO2). This research focused on pollutant variables that cause damage to human health as well as to the environment. Thus, seven statistical models {Weibull (W), Gamma (G), Lognormal (L), Frechet (Fr), Burr (Bur), Rayleigh (R) and Rician (Ri)} were chosen to fit the observations of the air pollutants. An average hourly data from one year to 2015 were considered. In addition, performance indicators {Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE)} were applied, to determine the quality criteria for adjustment of the frequency distributions. The best distribution that adapts to the observations of the variables was the RICIAN distribution, the log-normal distribution for COD. The probabilities of the concentration of exceedances were calculated,(predicted) from the cumulative density function (cdf) obtained from the best fit distributions.

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Background: Necessity of highway infrastructure development has been renowned all over the globe. Key success for highway projects mainly depends on the characterization of subgrade soil intended for thousands of kilometers. In practice, any one of the soil test data may not provide exact characterization of subgrade and at least minimum two tests can be used to develop design values of subgrade for a highway pavement. Also, there is a need to have relationship between two to three soil parameters so as to understand evidently about the soil characteristics and their behavior. Methods: Two test data such as CPT and DCP are utilized to develop statistical correlations for better site characterization. Ordinary least squares and the simple arithmetic mean methods are obtained for scatter plots of data pairs and different trends are fitted to the data. Correlation agreements between CPT and DCP for various combinations are plotted for 40 data sets. Results: Liquid limit values ranged from 22 to 56 %, while plastic limits are ranging in between 16 and 43 %. Plasticity index values are varying from minimum 1 % to maximum of 29 %, indicating low to medium swelling potential. Based on the American Association of State Highways and Transportation Officials soil classification system, soil along the chosen highway alignment includes A-2-4, A-4, A-2-5, A-2-7, A-1(a), A-1(b), A-7-6 and A-6. Similarly, according to Unified Soil Classification System, the dominant soils along the highway stretched are placed into inorganic silts or organic clays (MH or OH), inorganic clays (CL), inorganic silts or organic silts (ML and OL), and combinations of the two (CL-ML). Deliberation of sleeve friction measurements resulted minor improvement in correlations and these may be considered trivial. According to Roberson's chart, the distribution of CPT and DCP data obtained along the highway route encompasses four zones. Zone 4 (i.e., silt mixtures: clayey silt to silty clay), zone 5 (sand mixtures: silty sand to sandy silt), zone 6 (sands: clean sand to silty sand), and zone 8 (very stiff sand to clayey sand), with some scattered data points are located in zone 9 (very stiff, fine-grained). Conclusions: The correlations developed in this study indicates that, CPT (q c + f s) and DCP (q c) correlations are very much enhanced compared to other combinations studied in terms of higher coefficient of correlation and least transformation uncertainty. The CPT and DCP data obtained along the highway route is superimposed in Roberson's chart to characterize the subgrade soil swiftly.