The recognition that environmental pollution is a worldwide threat to public health and environmental degradation has given rise to new initiatives for environmental restoration for both economic and ecological reasons. There are several methods to treat the dye contaminated industrial wastewater; of which biological treatment methods are economical and environmentally friendly. The bacteria and fungi remediation of dye pollutants has been well characterized over a period of more than 30 years. So, finding other biological methods in addition to bacteria and fungi is great important in the world. As a result, investigating and evaluating Phycoremediation techniques of dye wastewater (bioremediation using Microalgae) have gained a great deal of attention because of their versatility and capacity than bacteria and fungi. The aim of the research is to study Phycoremediation of Textile Wastewater Using indigenous Microalgae. Physico-chemical parameters such as color, pH, total dissolved solid (TDS), biochemical oxygen demand (BOD) and chemical oxygen demand of the waste were determined with ASTM standard methods before and after bioremediation. Photo bioreactor systems were used for Phycoremediation treatment techniques. PH, incubation time and temperature effects were determined on a photo bioreactor treatment and optimal experimental condition was ascertained. Instrumental analytical techniques (UV-Vis, FTIR) were used to determine percent decolorizations of dye wastewater before and after bioremediation; and the actual break down of the dye functional groups. The maximum reductions of the basic parameters; COD, BOD and TDS were obtained 91.50%, 91.90% and 89.10% respectively. The optimum operating conditions in the photo bioreactor system were found incubation time 20 days, 30°C; with 10% of inoculums at a pH of 8. Under these conditions, a maximum of 82.6% decolorization was achieved in 20 days. The experimental investigations evidently tell us algae undoubtedly have the potential to rapidly, efficiently and effectively remove dyes wastewater.

Studying Libo-kemkem Woreda households’ perceptions and responses to climate change and variability was the primary focus of this study. A cross-sectional and primary data collection method was used in this study to address its main objective. Based on stratified and straightforward random selection methods, 216 rural households were selected for the study. Through the household survey, data was collected on perceptions of climate change, variability, and adaptation methods. To analyze the collected data, beta regression models, F-tests, and chi-squared tests were employed. The results showed that only 3.2% of respondents did not recognize the occurrence of climate change or variability, which means that 96.8% of respondents are aware of the phenomenon. According to the sample of respondents, temperatures grew by 91.9% and rainfall decreased by 88.8%. A total of 96.2% of the respondents noted that rainfall distribution was uneven in the study area. A survey revealed that almost 96.7% of respondents said climate change negatively impacts agriculture, animal output, water quality, and epidemic disease outbreaks. Additionally, the model showed that, rather than respondents’ ages, factors like educational attainment, income earned on and off the farm, farm size, access to extension services, and weather information impacted climate change adaptation measures statistically significantly and favorably. Policymakers, woreda agricultural offices, and development staff need to take statistically significant factors into account when developing and implementing adaptation plans for climate change and variability.

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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