In this study, the quality of collected rainwater at a downtown middle school rainwater harvesting system was evaluated by measuring physical, chemical, and microbiological parameters such as pH, dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), NO3, PO4, total coliform (TC), Escherichia coli , and some metals (i.e. Al, Cr, Mn, Zn, Cu, As, Cd, and Pb) (2003 to 2011). The analysis shows that the collected water quality is poor, which presents health, considering the high levels of bacterial indicators detected in the harvested rainwater, i.e. turbidity (1.4 to 15.5 NTU) and E. coli (120 and 35 CFU/100 mL in 2007 to 210 and 60 CFU/100 mL in 2011). This study shows that deteriorating water quality was caused by system contamination due to the absence of maintenance. Based on this study, proper operation and maintenance are generally the simplest and most effective ways of maintaining water quality.

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Recently, damages such as internal system intrusion, network and device vulnerability attacks, malicious code infection, and information leakage due to security attacks are increasing within the smart grid environment. Detailed and dynamic access control must be implemented to enable the power system in the smart grid environment to respond to such attacks. Dynamic and partial delegation must be available, and permission role restrictions must be considered for dynamic access control when delegating a role because of changes in power resource manager authority. In this paper, we propose an intelligent access control framework that can recognize security context by analyzing security vulnerabilities for security management of power systems. The intelligent access control framework is designed as a framework that enables collaboration within the smart grid environment, and a system administrator is designed to transmit access control policy information required between the power service principal and the agent. In addition, an experiment is conducted for the control inference of security context ontology-based access, attack detection inference of the security context awareness service, and the attack response of the intelligent integrated access control system. Experimental results show that the precision of security context ontology-based access control inference is 70%, and the attack response rate of integrated access control is 72.8%.

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