Access to clean and safe drinking water is a fundamental human requirement. However, in many areas of the world natural water sources have been impacted by a variety of biological and chemical contaminants. The ingestion of these contaminants may cause acute or chronic health problems. To prevent such illnesses, many technologies have been developed to treat, disinfect and supply safe drinking water quality. However, despite these advancements, water supply distribution systems can adversely affect the drinking water quality before it is delivered to consumers. The primary aim of this research was to investigate the effect that water distribution systems may have on household drinking water quality in Christchurch, New Zealand and Addis Ababa, Ethiopia. Water samples were collected from the source water and household taps in both cities. The samples were then tested for various physical, chemical and biological water quality parameters. The data collected was also used to determine if water samples complied with national drinking water quality standards in both countries. Independent samples t-test statistical analyses were also performed to determine if water quality measured in the samples collected from the source and household taps was significantly different. Water quality did not vary considerably between the source and tap water samples collected in Christchurch City. No bacteria were detected in any sample. However, the pH and total iron concentrations measured in source and tap water samples were found to be significantly different. The lower pH values measured in tap water samples suggests that corrosion may be taking place in the distribution system. No water samples transgressed the Drinking Water Standards for New Zealand (DWSNZ) MAVs. Monitoring data collected by the Christchurch City Council (CCC) was also used for comparison. A number of pH, turbidity and total iron concentration measurements collected by the CCC in 2011 were found to exceed the guideline values. This is likely due to structural damage to the source wells and pump-stations that occurred during the 2011 earthquake events. Overall, it was concluded that the distribution system does not adversely affect the quality of Christchurch City’s household drinking water. The water quality measured in samples collected from the source (LTP) and household taps in Addis Ababa was found to vary considerably. The water collected from the source complied with the Ethiopian (WHO) drinking water quality standards. However, tap water samples were often found to have degraded water quality for the physical and chemical parameters tested. This was especially the case after supply interruption and reinstatement events. Bacteria were also often detected in household tap water samples. The results from this study indicate that water supply disruptions may result in degraded water quality. This may be due to a drop in pipeline pressure and the intrusion of contaminants through the leaky and cross-connected pipes in the distribution network. This adversely affects the drinking water quality in Addis Ababa.
When disasters and crises, both man-made and natural, occur, resilient higher education institutions adapt in order to continue teaching and research. This may necessitate the closure of the whole institution, a building and/or other essential infrastructure. In disasters of large scale the impact can be felt for many years. There is an increasing recognition of the need for disaster planning to restructure educational institutions so that they become more resilient to challenges including natural disasters (Seville, Hawker, & Lyttle, 2012).The University of Canterbury (UC) was affected by seismic events that resulted in the closure of the University in September 2010 for 10 days and two weeks at the start of the 2011 academic year This case study research describes ways in which e-learning was deployed and developed by the University to continue and even to improve learning and teaching in the aftermath of a series of earthquakes in 2010 and 2011. A qualitative intrinsic embedded/nested single case study design was chosen for the study. The population was the management, support staff and educators at the University of Canterbury. Participants were recruited with purposive sampling using a snowball strategy where the early key participants were encouraged to recommend further participants. Four sources of data were identified: (1) documents such as policy, reports and guidelines; (2) emails from leaders of the colleges and academics; (3) communications from senior management team posted on the university website during and after the seismic activity of 2010 and 2011; and (4) semi-structured interviews of academics, support staff and members of senior management team. A series of inductive descriptive content analyses identified a number of themes in the data. The Technology Acceptance Model 2 (Venkatesh & Davis, 2000) and the Indicator of Resilience Model (Resilient Organisations, 2012) were used for additional analyses of each of the three cases. Within the University case, the cases of two contrasting Colleges were embedded to produce a total of three case studies describing e-learning from 2000 - 2014. One contrast was the extent of e-learning deployment at the colleges: The College of Education was a leader in the field, while the College of Business and Law had relatively little e-learning at the time of the first earthquake in September 2010. The following six themes emerged from the analyses: Communication about crises, IT infrastructure, Availability of e-learning technologies, Support in the use of e-learning technologies, Timing of crises in academic year and Strategic planning for e-learning. One of the findings confirmed earlier research that communication to members of an organisation and the general public about crises and the recovery from crises is important. The use of communication channels, which students were familiar with and already using, aided the dissemination of the information that UC would be using e-learning as one of the options to complete the academic year. It was also found that e-learning tools were invaluable during the crises and facilitated teaching and learning whilst freeing limited campus space for essential activities and that IT infrastructure was essential to e-learning. The range of e-learning tools and their deployment evolved over the years influenced by repeated crises and facilitated by the availability of centrally located support from the e-Learning support team for a limited set of tools, as well as more localised support and collaboration with colleagues. Furthermore, the reasons and/or rate of e-learning adoption in an educational institution during crises varied with the time of the academic year and the needs of the institution at the time. The duration of the crises also affected the adoption of e-learning. Finally, UC’s lack of an explicit e-learning strategy influenced the two colleges to develop college-specific e-learning plans and those College plans complemented the incorporation of e-learning for the first time in the University’s teaching and learning strategy in 2013. Twelve out of the 13 indicators of the Indicators of Resilience Model were found in the data collected for the study and could be explained using the model; it revealed that UC has become more resilient with e-learning in the aftermath of the seismic activities in 2010 and 2011. The interpretation of the results using TAM2 demonstrated that the adoption of technologies during crises aided in overcoming barriers to learning at the time of the crisis. The recommendations from this study are that in times of crises, educational institutions take advantage of Cloud computing to communicate with members of the institution and stakeholders. Also, that the architecture of a university’s IT infrastructure be made more resilient by increasing redundancy, backup and security, centralisation and Cloud computing. In addition, when under stress it is recommended that new tools are only introduced when they are essential.
