LUMB, ASHOK, HALLIWELL, DOUG, TRIBENI, SHARMA (2006),. Application of CCMEwater Quality Index to monitor water quality: a case of the Mackenzie river ...
Water Quality Management Upstream Cairo Drinking Water Plants along the Nile River A Thesis Submitted in Partial Fulfillment for the Requirements of the Ph.D Degree of Science in Civil Engineering (Irrigation and Hydraulics) By
Mohamed Ahmed Reda Hamed M.Sc. Civil Engineering -(Sanitary and Environmental Engineering) Ain Shams University (2013)
Supervised by Prof. Dr. Abdel Kawi Khalifa Professor of Hydraulics Irrigation & Hydraulics Department Faculty of Engineering -Ain Shams University
Prof. Dr. Mohamed Nour El-Deen Professor of Hydraulics Irrigation & Hydraulics Department Faculty of Engineering -Ain Shams University
Prof. Dr. Mohammed Hassan Abd El-Razik Professor of Sanitary & Environmental Department Faculty of Engineering - Ain Shams University
Dr. Hussein El Gammal Associate Professor, National Water Research Center Ministry of Water Resources and Irrigation
Dr. Peter Hany Sobhy Riad Lecturer, Irrigation and Hydraulics Department Faculty of Engineering, Ain Shams University
Cairo, Egypt, 2016
Ain Shams University Faculty Of Engineering
Water Quality Management Upstream Cairo Drinking Water Plants along the Nile River by
Eng. Mohamed Ahmed Reda Hamed A Thesis Submitted for the Partial Fulfillment of the Doctor of Philosophy
Examiners’ Committee
Name and Affiliation
Signature
Prof. Dr. Abdel Kawi Khalifa Professor of Hydraulics Irrigation and Hydraulics Department Faculty of Engineering Ain Shams University
Prof. Dr. Mohamed Nour El-Deen Professor of Hydraulics Irrigation and Hydraulics Department Faculty of Engineering Ain Shams University
Date:
Researcher Data Name
: Mohamed Ahmed Reda Hamed
Date of birth
: 7/11/1971
Place of birth
: Cairo
Last academic degree
: M.Sc. in Civil Engineering.
Field of specialization
: Sanitary and Environmental
Department. University issued the degree
: Ain Shams University
Date of issued degree
: 3/2013
Current job
: Technical Office Engineer, Greater Cairo Drinking Water Company
Statement This thesis is submitted to the Irrigation and Hydraulics Department, Faculty of Engineering, Ain Shams University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Civil Engineering. The work included in this thesis was carried out by the author in the Irrigation and Hydraulics Department, Faculty of Engineering, Ain Shams University from 2013 to 2016. No part of this thesis has been submitted for a degree or a qualification at any other university or institution. Name: Mohamed Ahmed Reda Hamed Signature: Date:
Acknowledgment First of all, I would express all my gratitude to ALLAH almighty for Blessing this work until it has reached its end, as a part of his generous Help throughout my life. I would like to express my deepest gratitude to my dear Professor Dr.Mohamed Nour El-Deen, Professor of Hydraulics, Ain Shams University, for his continuous guidance, expertise advice and valuable suggestions that greatly enriched this work. I am deeply grateful to the kindness of Professor Dr. Abdel Kawi Ahmed Mokhtar Khalifa, Professor of Hydraulics, Ain Shams University, for his continuous generous, sincere contributions to this work and valuable efforts throughout this study. I am much obliged to Dr. Peter Sobhy Riad, Professor of Irrigation and Hydraulics, Ain Shams University, for his valuable suggestions that greatly enriched this work. Last but not least, I would like to thank Dr. Hussein El Gammal, Associate Professor, Secretary General of the National Water Research Center, Ministry of Water Resources and Irrigation, for his continuous generous, sincere contributions to this work and valuable efforts throughout this study. Mohamed A Reda.
i
ABSTRACT Deteriorating water quality is a particular threat in countries with scarce water resources as the case of Egypt. Cairo, sits on the River Nile south of the Mediterranean Sea, has an average reach length along the river about 50 km (from Km 900 to km 950 referenced to Aswan High Dam). This area is a particular importance in the study of surface water quality because of industrial, municipal and agricultural wastes were mixing with river flow and surrounding water body thereby deteriorating the water quality. However, Cairo Drinking Water Plants (CDWPs) that takes their raw water source from Nile river need a particular attention and continuous control for their water source quality to prevent health hazards. This study mainly aims to develop Water Quality Management Information System (WQMIS) capable of proposing the required management scenarios to improve water quality upstream CDWPs and control the pollution sources. The work tasks can be divided into three phases. In the first phase water quality index (WQI) was calculated using Canadian Water Quality Index (CWQI) in order to evaluate the water quality upstream Cairo drinking water plants. In the second phase, the mathematical model (MIKE11) was formulated to simulate various water quality parameters. In the second phase, different scenarios were proposed to predict water quality improvement. An integrated evaluation framework is developed using analytical hierarchy process of Multi Criteria Analysis (MCA) that takes four indicators into account; technical, environmental, economical and socio-community for evaluation and ranking various water quality management scenarios. MCA for different scenarios showed that the water quality management scenario focusing on treatment of CDWPs sludge instead of discharging it to Nile river is the most convenient scenario. In the third phase, WQMIS was constructed by using Microsoft Visual C programming applications to store required data for assessing and predicting the situation of the water quality status under current and future conditions. Based on the results of this study, the developed WQMIS can be used as an effective tool to facilitate assessing , predicting water pollution and can provide easier decision making process for achieving designated water quality objectives. Keywords Surface Water, Drinking Water Plants, CWQI, MIKE11, MCA, WQMIS. ii
ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ -ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ ﻗﺴﻢ ﺍﻟﺮﻯ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ
ﺇﺩﺍﺭﺓ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﺃﻣﺎﻡ ﻣﺤﻄﺎﺕ ﻣﻴﺎﻩ ﺍﻟﺸﺮﺏ ﺑﺎﻟﻘﺎﻫﺮﺓ ﻋﻠﻰ ﻧﻬﺮ ﺍﻟﻨﻴﻞ ﺭﺳﺎﻟﺔ ﻣﻘﺪﻣﺔ ﻟﻠﺤﺼﻮﻝ ﻋﻠﻰ ﺩﺭﺟﺔ ﺍﻟﺪﻛﺘﻮﺭﺍﻩ ﻓﻰ ﺍﻟﻬﻨﺪﺳﺔ ﺍﻟﻤﺪﻧﻴﺔ – ﺭﻯ ﻭﻫﻴﺪﺭﻭﻟﻴﻜﺎ
ﻣﻘﺪﻣﺔ ﻣﻦ
ﷴ ﺍﺣﻤﺪ ﺭﺿﺎ ﺣﺎﻣﺪ
ﻣﺎﺟﺴﺘﻴﺮ ﺍﻟﻬﻨﺪﺳﺔ ﺍﻟﻤﺪﻧﻴﺔ )ﻫﻨﺪﺳﺔ ﺻﺤﻴﺔ ﻭﺑﻴﺌﻴﺔ( 2013- ﺗﺤﺖ ﺇﺷﺮﺍﻑ ﺍﻷﺳﺘﺎﺫ ﺍﻟﺪﻛﺘﻮﺭ /ﻋﺒﺪ ﺍﻟﻘﻮﻱ ﺍﺣﻤﺪ ﻣﺨﺘﺎﺭ ﺧﻠﻴﻔﺔ ﺃﺳﺘﺎﺫ ﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ -ﻗﺴﻢ ﺍﻟﺮﻯ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ
ﺍﻷﺳﺘﺎﺫ ﺍﻟﺪﻛﺘﻮﺭ /ﷴ ﷴ ﻧﻮﺭ ﺍﻟﺪﻳﻦ ﻋﻮﻳﺲ ﺃﺳﺘﺎﺫ ﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ -ﻗﺴﻢ ﺍﻟﺮﻯ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ
ﺍﻷﺳﺘﺎﺫ ﺍﻟﺪﻛﺘﻮﺭ /ﷴ ﺣﺴﻦ ﺗﻮﻓﻴﻖ ﻋﺒﺪ ﺍﻟﺮﺍﺯﻕ ﺃﺳﺘﺎﺫ ﺍﻟﻬﻨﺪﺳﺔ ﺍﻟﺼﺤﻴﺔ ﻭﺍﻟﺒﻴﺌﻴﺔ ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ
ﺍﻟﺪﻛﺘﻮﺭ /ﺣﺴﻴﻦ ﻋﺒﺪ ﺍﻟﺤﻠﻴﻢ ﺍﻟﺠﻤﺎﻝ
ﺍﻷﻣﻴﻦ ﺍﻟﻌﺎﻡ ﻟﻠﻤﺮﻛﺰ ﺍﻟﻘﻮﻣﻲ ﻟﺒﺤﻮﺙ ﺍﻟﻤﻴﺎﻩ ﻭﺯﺍﺭﺓ ﺍﻟﻤﻮﺍﺭﺩ ﺍﻟﻤﺎﺋﻴﺔ ﻭﺍﻟﺮﻱ
ﺍﻟﺪﻛﺘﻮﺭ/ﺑﻴﺘﺮ ﻫﺎﻧﻲ ﺻﺒﺤﻲ ﺭﻳﺎﺽ ﻣﺪﺭﺱ ﺑﻘﺴﻢ ﺍﻟﺮﻱ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ
ﺍﻟﻘﺎﻫﺮﺓ -ﻣﺼﺮ
2016
ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ ﻗﺴﻢ ﺍﻟﺮﻱ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ ﺭﺳﺎﻟﺔ ﺍﻟﺪﻛﺘﻮﺭﺍﻩ: ﺍﺳﻢ ﺍﻟﻄﺎﻟﺐ
:ﷴ ﺍﺣﻤﺪ ﺭﺿﺎ ﺣﺎﻣﺪ
ﻋﻨﻮﺍﻥ ﺍﻟﺮﺳﺎﻟﺔ :ﺇﺩﺍﺭﺓ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﺃﻣﺎﻡ ﻣﺤﻄﺎﺕ ﻣﻴﺎﻩ ﺍﻟﺸﺮﺏ ﺑﺎﻟﻘﺎﻫﺮﺓ ﻋﻠﻰ ﻧﻬﺮ ﺍﻟﻨﻴﻞ ﺍﺳﻢ ﺍﻟﺪﺭﺟﺔ
:ﺩﻛﺘﻮﺭﺍﻩ ﺍﻟﻔﻠﺴﻔﺔ ﻓﻲ ﺍﻟﻬﻨﺪﺳﺔ ﺍﻟﻤﺪﻧﻴﺔ -ﻗﺴﻢ ﺭﻱ ﻭﻫﻴﺪﺭﻭﻟﻴﻜﺎ
ﻟﺠﻨﺔ ﺍﻹﺷﺮﺍﻑ ﺃ.ﺩ .ﻋﺒﺪ ﺍﻟﻘﻮﻱ ﺍﺣﻤﺪ ﻣﺨﺘﺎﺭ ﺧﻠﻴﻔﺔ ﺃﺳﺘﺎﺫ ﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ -ﻗﺴﻢ ﺍﻟﺮﻯ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ -ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ ﺃ.ﺩ .ﷴ ﷴ ﻧﻮﺭ ﺍﻟﺪﻳﻦ ﻋﻮﻳﺲ ﺃﺳﺘﺎﺫ ﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ ﻗﺴﻢ ﺍﻟﺮﻯ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ -ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ ﺍﻷﺳﺘﺎﺫ ﺍﻟﺪﻛﺘﻮﺭ /ﷴ ﺣﺴﻦ ﺗﻮﻓﻴﻖ ﻋﺒﺪ ﺍﻟﺮﺍﺯﻕ
ﺃﺳﺘﺎﺫ ﺍﻟﻬﻨﺪﺳﺔ ﺍﻟﺼﺤﻴﺔ ﻭﺍﻟﺒﻴﺌﻴﺔ -ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ
ﺍﻟﺪﻛﺘﻮﺭ /ﺣﺴﻴﻦ ﻋﺒﺪ ﺍﻟﺤﻠﻴﻢ ﺍﻟﺠﻤﺎﻝ
ﺍﻷﻣﻴﻦ ﺍﻟﻌﺎﻡ ﻟﻠﻤﺮﻛﺰ ﺍﻟﻘﻮﻣﻲ ﻟﺒﺤﻮﺙ ﺍﻟﻤﻴﺎﻩ -ﻭﺯﺍﺭﺓ ﺍﻟﻤﻮﺍﺭﺩ ﺍﻟﻤﺎﺋﻴﺔ ﻭﺍﻟﺮﻱ
ﺍﻟﺪﻛﺘﻮﺭ/ﺑﻴﺘﺮ ﻫﺎﻧﻲ ﺻﺒﺤﻲ ﺭﻳﺎﺽ
ﻣﺪﺭﺱ ﺑﻘﺴﻢ ﺍﻟﺮﻱ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ -ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ -ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ
ﺗﺎﺭﻳﺦ ﺍﻟﺒﺤﺚ / / : ﺍﻟﺪﺭﺍﺳﺎﺕ ﺍﻟﻌﻠﻴﺎ: ﺧﺘﻢ ﺍﻹﺟﺎﺯﺓ: ﺃﺟﻴﺰﺕ ﺍﻟﺮﺳﺎﻟﺔ ﺑﺘﺎﺭﻳﺦ / : ﻣﻮﺍﻓﻘﺔ ﻣﺠﻠﺲ ﺍﻟﻜﻠﻴﺔ / : ﻣﻮﺍﻓﻘﺔ ﻣﺠﻠﺲ ﺍﻟﺠﺎﻣﻌﺔ / :
/ /
/
ﺍﻟﻘﺎﻫﺮﺓ 2015-
ﺍﻟﻤﻮﺍﻓﻘﺔ ﻋﻠﻰ ﺍﻟﻤﻨﺢ ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ ﻗﺴﻢ ﺍﻟﺮﻯ ﻭﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﺎ
ﺇﺩﺍﺭﺓ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﺃﻣﺎﻡ ﻣﺤﻄﺎﺕ ﻣﻴﺎﻩ ﺍﻟﺸﺮﺏ ﺑﺎﻟﻘﺎﻫﺮﺓ ﻋﻠﻰ ﻧﻬﺮ ﺍﻟﻨﻴﻞ ﺇﻋﺪﺍﺩ ﷴ ﺍﺣﻤﺪ ﺭﺿﺎ ﺣﺎﻣﺪ ﻋﺒﺪ ﷲ
ﻟﺠﻨﺔ ﺍﻟﺤﻜﻢ ﺍﻻﺳﻢ
ﺍﻟﺘﻮﻗﻴﻊ
ﺃ.ﺩ .ﻋﺒﺪ ﺍﻟﻘﻮﻱ ﺍﺣﻤﺪ ﻣﺨﺘﺎﺭ ﺧﻠﻴﻔﺔ ﺃ.ﺩ .ﷴ ﷴ ﻧﻮﺭ ﺍﻟﺪﻳﻦ ﻋﻮﻳﺲ
2015/ /
ﺗﻌﺮﻳﻒ ﺑﻤﻘﺪﻡ ﺍﻟﺮﺳﺎﻟﺔ ﺍﻻﺳﻢ
:
ﷴ ﺍﺣﻤﺪ ﺭﺿﺎ ﺣﺎﻣﺪ ﻋﺒﺪ ﷲ
ﺗﺎﺭﻳﺦ ﺍﻟﻤﻴﻼﺩ
:
1971/11/7
ﻣﺤﻞ ﺍﻟﻤﻴﻼﺩ
:
ﺍﻟﻘﺎﻫﺮﺓ
ﺃﺧﺮ ﺩﺭﺟﺔ ﺟﺎﻣﻌﻴﺔ
:ﻣﺎﺟﺴﺘﻴﺮ ﺍﻟﻬﻨﺪﺳﺔ ﺍﻟﻤﺪﻧﻴﺔ– ﻫﻨﺪﺳﺔ ﺻﺤﻴﺔ ﻭﺑﻴﺌﻴﺔ – ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ
ﺍﻟﺠﻬﺔ ﺍﻟﻤﺎﻧﺤﺔ
:ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ
ﺗﺎﺭﻳﺦ ﺍﻟﻤﻨﺢ
2013/3 :
ﺍﻟﻮﻅﻴﻔﺔ ﺍﻟﺤﺎﻟﻴﺔ
:ﻣﻬﻨﺪﺱ ﺑﺸﺮﻛﺔ ﻣﻴﺎﻩ ﺍﻟﺸﺮﺏ ﺑﺎﻟﻘﺎﻫﺮﺓ
ﺟﺎﻣﻌﺔ ﻋﻴﻦ ﺷﻤﺲ ﻛﻠﻴﺔ ﺍﻟﻬﻨﺪﺳﺔ ﻣﻘﺪﻡ ﺍﻟﺮﺳﺎﻟﺔ :ﷴ ﺍﺣﻤﺪ ﺭﺿﺎ ﺣﺎﻣﺪ ﻋﻨﻮﺍﻥ ﺍﻟﺮﺳﺎﻟﺔ :ﺇﺩﺍﺭﺓ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﺃﻣﺎﻡ ﻣﺤﻄﺎﺕ ﻣﻴﺎﻩ ﺍﻟﺸﺮﺏ ﺑﺎﻟﻘﺎﻫﺮﺓ ﻋﻠﻰ ﻧﻬﺮ ﺍﻟﻨﻴﻞ
ﻣﺴﺘﺨﻠﺺ ﺍﻟﺒﺤﺚ ﻣﻊ ﺗﺰﺍﻳﺪ ﺍﻟﻨﻤﻮ ﺍﻟﺴ�ﻜﺎﻧﻲ ﻭﺍﻟﺘﻐﻴ�ﺮﺍﺕ ﺍﻟﻤﻨﺎﺧﻴ�ﺔ ﺍﻟﻌﺎﻟﻤﻴ�ﺔ ﻭﺗﺴ�ﺎﺭﻉ ﻛﺎﻓ�ﺔ ﺍﻷﻧﺸ�ﻄﺔ ﺍﻟﺒﺸ�ﺮﻳﺔ ﻟﻤﻮﺍﻛﺒ�ﺔ ﻣﻨﻈﻮﻣﺔ ﺍﻟﺘﻨﻤﻴﺔ ﺍﻻﻗﺘﺼﺎﺩﻳﺔ ﺍﺯﺩﺍﺩﺕ ﻣﻌﻪ ﺍﻟﺤﺎﺟ�ﺔ ﻟﻠﻌﻨﺎﻳ�ﺔ ﺑﺎﻟﻤﺤﺎﻓﻈ�ﺔ ﻋﻠ�ﻰ ﺟ�ﻮﺩﺓ ﻭ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ ﺍﻟﺴﻄﺤﻴﺔ ﻓﻲ ﻣﺼﺮ .ﻭﻓﻲ ﻅﻞ ﻗﻠﺔ ﻛﻤﻴﺎﺕ ﺍﻻﻣﻄﺎﺭ ﻓﻲ ﺍﻟﺒﻼﺩ ﺍﺯﺩﺍﺩ ﺍﻻﻋﺘﻤ�ﺎﺩ ﺑﺸ�ﻜﻞ ﺍﺳﺎﺳ�ﻲ ﻋﻠ�ﻰ ﻧﻬﺮ ﺍﻟﻨﻴﻞ ،ﺍﻷﻣﺮ ﺍﻟﺬﻱ ﺃﺩﻯ ﺇﻟﻲ ﺍﻋﺘﺒﺎﺭ ﺇﺩﺍﺭﺓ ﻣﻨﻈﻮﻣﺔ ﺟﻮﺩﺓ ﻣﻴ�ﺎﻩ ﻧﻬ�ﺮ ﺍﻟﻨﻴ�ﻞ ﺍﺣ�ﺪ ﺍﻫ�ﻢ ﺍﻟﺘﺤ�ﺪﻳﺎﺕ ﺍﻟﺒﻴﺌﻴﺔ ﺍﻟﻬﺎﻣﺔ ﺍﻟﺤﺎﻟﻴﺔ .ﻟﺬﺍ ﻳﺮﻛ�ﺰ ﻫ�ﺬﺍ ﺍﻟﺒﺤ�ﺚ ﻋﻠ�ﻰ ﺍﻟﻨﻄ�ﺎﻕ ﺍﻟﺠﻐﺮﺍﻓ�ﻲ ﻟﻤﺤﺎﻓﻈ�ﺔ ﺍﻟﻘ�ﺎﻫﺮﺓ ﻭﺍﻟﻮﺍﻗﻌ�ﺔ ﺑﺪﺍﻳﺔ ﻣﻦ ﺍﻟﻜﻴﻠﻮ ﻣﺘﺮ 900.00ﻭﺣﺘ�ﻰ ﺍﻟﻜﻴﻠ�ﻮ ﻣﺘ�ﺮ 950.00ﻋﻠ�ﻰ ﻁ�ﻮﻝ ﺧ�ﻂ ﺍﻟﻨﻴ�ﻞ ﻭﻳﻤﺘ�ﺪ ﻧﻄ�ﺎﻕ ﺍﻟﺪﺭﺍﺳﺔ ﻛ�ﺬﻟﻚ ﺟﻨﻮﺑ�ﺎ ﺣﺘ�ﻰ ﻣﺪﻳﻨ�ﺔ ﺍﻟﺼ�ﻒ )ﺍﻟﻜﻴﻠ�ﻮ ﻣﺘ�ﺮ ( 877.00ﻭﺷ�ﻤﺎﻻ ﺣﺘ�ﻰ ﻣﺪﻳﻨ�ﺔ ﺍﻟﻘﻨ�ﺎﻁﺮ )ﺍﻟﻜﻴﻠ��ﻮ ﻣﺘ��ﺮ .(953.00ﻭﺗﺤﻈ��ﻰ ﻣﻨﻄﻘ��ﺔ ﺍﻟﺪﺭﺍﺳ��ﺔ ﺑﺎﻟﻤﺰﻳ��ﺪ ﻣ��ﻦ ﺍﻻﻫﺘﻤ��ﺎﻡ ﻭﺫﻟ��ﻚ ﻧﻈ��ﺮﺍ ﻟﺘﻌﺮﺿ��ﻬﺎ ﻟﻠﻌﺪﻳﺪ ﻣﻦ ﻣﺼﺎﺩﺭ ﺍﻟﺘﻠﻮﺙ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻭﺍﻟﺘﻲ ﻗﺪ ﺗﺆﺛﺮ ﺳﻠﺒﻴﺎ ﻋﻠﻰ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﺍﻟﺴ�ﻄﺤﻴﺔ ﻟﻨﻬ�ﺮ ﺍﻟﻨﻴ�ﻞ. ﻭﻳﻬ��ﺪﻑ ﻫ��ﺬﺍ ﺍﻟﺒﺤ��ﺚ ﺍﻟ��ﻲ ﺇﺩﺍﺭﺓ ﻭﻧﻤﺬﺟ��ﺔ ﻧﻮﻋﻴ��ﺔ ﺍﻟﻤﻴ��ﺎﻩ ﺍﻟﺴ��ﻄﺤﻴﺔ ﻟﻨﻬ��ﺮ ﺍﻟﻨﻴ��ﻞ ﺃﻣ��ﺎﻡ ﻣﺤﻄ��ﺎﺕ ﻣﻴ��ﺎﻩ ﺍﻟﺸ�ﺮﺏ ﺑﺎﻟﻘ�ﺎﻫﺮﺓ ﺑﻤﻨﻄﻘ�ﺔ ﺍﻟﺪﺭﺍﺳ�ﺔ ،ﻭﺗ�ﻢ ﺗﻘﺴ�ﻴﻢ ﺍﻟﻌﻤ�ﻞ ﻓ�ﻰ ﺍﻟﺒﺤ�ﺚ ﺇﻟ�ﻰ ﺛﻼﺛ�ﺔ ﺃﺟ�ﺰﺍء .ﺃﻭﻻً ﺟﻤ�ﻊ ﺍﻟﺒﻴﺎﻧﺎﺕ ﻋﻦ ﻣﺼﺎﺩﺭ ﺗﻠﻮﺙ ﻣﻴ�ﺎﻩ ﻧﻬ�ﺮ ﺍﻟﻨﻴ�ﻞ ﺑﻤﻨﻄﻘ�ﺔ ﺍﻟﺪﺭﺍﺳ�ﺔ ﻭﺇﺟ�ﺮﺍء ﻣﺨﺘﻠ�ﻒ ﺍﻟﺘﺤﺎﻟﻴ�ﻞ ﺍﻟﻤﻌﻤﻠﻴ�ﺔ ﺍﻟﻼﺯﻣﺔ ﻟﺘﺮﻛﻴﺰﺍﺕ ﺍﻟﻌﻨﺎﺻﺮ ﺍﻟﻤﺤﺪﺩﺓ ﻟﻤﺪﻯ ﺟﻮﺩﺓ ﺍﻟﻤﻴﺎﻩ ﺛﻢ ﺣﺴ�ﺎﺏ ﻣﺆﺷ�ﺮ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ""WQI .ﺛﺎﻧﻴﺎ ً ﺍﺳﺘﺨﺪﺍﻡ ﺑﺮﻧ�ﺎﻣﺞ " " MIKE11ﻛﻨﻤ�ﻮﺫﺝ ﻟﻠﻤﺤﺎﻛ�ﺎﺓ ﻭﻫ�ﻮ ﺑﺮﻧ�ﺎﻣﺞ ﺗﻄﺒﻴﻘ�ﻰ ﻋﻠ�ﻰ ﺍﻟﺤﺎﺳ�ﺐ ﺍﻵﻟﻰ ﻟﺘﻤﺜﻴﻞ ﺍﻟﺘﺪﻓﻖ ﺍﻟﻤﺴﺘﻘﺮ ﻭﻛﺬﺍﻟﻚ ﺟﻮﺩﺓ ﺍﻟﻤﻴﺎﻩ ﻓﻰ ﺍﻟﻘﻨﻮﺍﺕ ﺍﻟﻤﻜﺸﻮﻓﺔ ،ﻛﻤﺎ ﻳﺴﺎﻋﺪ ﺍﻟﻨﻤ�ﻮﺫﺝ ﻓ�ﻲ ﺍﻟﺘﻨﺒﺆ ﺑﻘﻴﻤ�ﺔ ﺗﺮﻛ�ﺰ ﺍﻟﻤﻠﻮﺛ�ﺎﺕ ﻣ�ﻊ ﺗﻐﻴ�ﺮ ﺍﻟﻈ�ﺮﻭﻑ ﻭﻛ�ﺬﻟﻚ ﻳﺴ�ﺎﻋﺪ ﺍﻟﻨﻤ�ﻮﺫﺝ ﻓ�ﻲ ﺇﺟ�ﺮﺍء ﺗﻘﻴ�ﻴﻢ ﻟﻘ�ﻴﻢ ﺗﺮﻛﻴﺰ ﺍﻟﻤﻠﻮﺛﺎﺕ ﻭﻣﻘﺎﺭﻧﺘﻬﺎ ﺑﺎﻟﺤﺪﻭﺩ ﺍﻟﻤﺴﻤﻮﺡ ﺑﻬﺎ ﻁﺒﻘﺎ ﻟﻠﻤﻌﺎﻳﻴﺮ ﻭﺍﻟﻘﻮﺍﻧﻴﻦ ﺍﻟﺒﻴﺌﻴﺔ،ﻭﻗﺪ ﺗﻢ ﻣﻌ�ﺎﻳﺮﺓ ﺍﻟﻨﻤ�ﻮﺫﺝ ﺑﻨﺠ�ﺎﺡ ﺑﺎﺳ�ﺘﺨﺪﺍﻡ ﻗﻴﺎﺳ�ﺎﺕ ﻟ�ﺒﻌﺾ ﺍﻟﻌﻨﺎﺻ�ﺮ ﺍﻟﻤﺤ�ﺪﺩﺓ ﻟﺠ�ﻮﺩﺓ ﺍﻟﻤﻴ�ﺎﻩ ﻭﻛ�ﺬﻟﻚ ﺛ�ﻢ ﺇﺟ�ﺮﺍء ﺍﻟﺴﻴﻨﺎﺭﻳﻮﻫﺎﺕ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻟﺘﺤﺴﻴﻦ ﻣﺴ�ﺘﻮﻯ ﺟ�ـﻮﺩﺓ ﺍﻟﻤﻴ�ﺎﻩ ﺃﻣ�ﺎﻡ ﻣﺤﻄ�ﺎﺕ ﻣﻴ�ﺎﻩ ﺍﻟﺸ�ﺮﺏ ﺑﺎﻟﻘﺎﻫ�ـﺮﺓ ﻣ�ﻊ ﺇﺟ��ﺮﺍء ﻋﻤﻠﻴ��ﺔ ﺍﻟﺘﻘﻴﻴ �ـﻢ ﺍﻟﺸ��ﺎﻣﻞ ﻭﺗﺮﺗﻴ��ﺐ ﺃﻭﻟﻮﻳ �ـﺎﺕ ﺗﻨﻔﻴ��ﺬ ﻣﺨﺘﻠ��ﻒ ﻫ��ﺬﻩ ﺍﻟﺴﻴﻨﺎﺭﻳﻮﻫ �ـﺎﺕ ﺑﺎﺳ��ﺘﺨﺪﺍﻡ ﺗﺤﻠﻴ���ـﻞ ﺍﻟﻤﻌ���ﺎﻳﻴﺮ ﺍﻟﻤﺘﻌ���ﺪﺩﺓ " "MCAﺑﻨ���ﺎء ﻋﻠ���ﻰ ﻣﻌﺎﻳﻴ���ـﺮ ﻓﻨﻴ���ﺔ ،ﺑﻴﺌﻴ���ﺔ ،ﺍﻗﺘﺼ���ﺎﺩﻳﺔ ،ﺻ���ﺤﻴﺔ ﻭﺍﺟﺘﻤﺎﻋﻴ��ﺔ ﻭﺃﺧﻴ��ﺮﺍ ﺗ��ﻢ ﺗﺼ��ﻤﻴﻢ ﻧﻈ��ﺎﻡ ﺍﻟﻤﻌﻠﻮﻣ��ﺎﺕ ﻹﺩﺍﺭﺓ ﻧﻮﻋﻴ��ﺔ ﺍﻟﻤﻴ��ﺎﻩ ﺑﻤﻨﻄﻘ��ـﺔ ﺍﻟﺪﺭﺍﺳ��ـﺔ ) (WQMISﺑﺎﺳﺘﺨ �ـﺪﺍﻡ ﺑﺮﻧ��ـﺎﻣﺞ " ، "Microsoft Visual C Programﻭﻳﺸ��ﻤﻞ ﻧﻈ��ﺎﻡ ﺍﻟﻤﻌﻠﻮﻣ��ﺎﺕ ﺍﻟﺠ��ﺪﺍﻭﻝ ﺍﻟﺨﺎﺻ��ﺔ ﺑﺘﺮﻛﻴ��ﺰ ﺍﻟﻤﻠﻮﺛ��ﺎﺕ ،ﻗﻴﺎﺳ��ﺎﺕ ﻧﻮﻋﻴ��ﺔ ﺍﻟﻤﻴ��ﺎﻩ ،ﺗﺮﻛﻴ��ﺰﺍﺕ ﺍﻟﻌﻨﺎﺻ��ﺮ ﻣﺼﺎﺩﺭ ﺍﻟﺘﻠﻮﺙ ﻭﺃﻳﻀﺎ ﺍﻟﺒﻴﺎﻧﺎﺕ ﺍﻟﺨﺎﺻﺔ ﺑﺈﺩﺍﺭﺓ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ ﺑﻤﻨﻄﻘ�ﺔ ﺍﻟﺪﺭﺍﺳ�ﺔ.ﺑﻨ�ﺎء ﻋﻠﻴ�ﻪ ﻳﻤﻜ�ﻦ ﻟﻤﺘﺨ�ﺬﻯ ﺍﻟﻘ�ﺮﺍﺭ ﺍﺳ�ﺘﺨﺪﺍﻡ ﻫ�ﺬﺍ ﺍﻟﺒﺮﻧ�ﺎﻣﺞ ﻛ�ﺄﺩﺍﺓ ﻹﺩﺍﺭﺓ ﺟ�ﻮﺩﺓ ﺍﻟﻤﻴ�ﺎﻩ ﻭﻣﺼ�ﺎﺩﺭ ﺍﻟﺘﻠ�ﻮﺙ ﻟﺘﺤﻘﻴ�ﻖ ﺍﻟﺘﺤﺴﻴﻦ ﺍﻟﻤﺴﺘﻬﺪﻑ ﻟﺠﻮﺩﺓ ﺍﻟﻤﻴﺎﻩ.
ﻛﻠﻤﺎﺕ ﺍﻟﻤﻔﺘﺎﺡ
ﺍﻟﻤﻴ��ﺎﻩ ﺍﻟﺴ��ﻄﺤﻴﺔ ،ﻣﺤﻄ��ﺎﺕ ﻣﻴ��ﺎﻩ ﺍﻟﺸ��ﺮﺏ ،ﻣﺆﺷ��ﺮ ﻧﻮﻋﻴ��ﺔ ﺍﻟﻤﻴ��ﺎﻩ ،MIKE11 ،ﺗﺤﻠﻴ��ـﻞ ﺍﻟﻤﻌ��ﺎﻳﻴﺮ ﺍﻟﻤﺘﻌﺪﺩﺓ ،ﻧﻈﺎﻡ ﺍﻟﻤﻌﻠﻮﻣﺎﺕ ﻹﺩﺍﺭﺓ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ.
ﻭﻗﺪ ﺍﺷﺘﻤﻠﺖ ﻫﺬﻩ ﺍﻟﺮﺳ�ﺎﻟﺔ ﻋﻠ�ﻰ ﺳ�ﺘﺔ ﺃﺑ�ﻮﺍﺏ ﺑﺎﻻﺿ�ﺎﻓﺔ ﺍﻟ�ﻲ ﺍﻟﺠ�ﺪﺍﻭﻝ ﻭﻗ�ﻮﺍﺋﻢ ﺍﻷﺷ�ﻜﺎﻝ ﻭﻛﺎﺷ�ﻔﺎﺕ ﺍﻟﺮﻣﻮﺯ ﻭﺍﻻﺧﺘﺼﺎﺭﺍﺕ ﻭﺍﻟﻤﺮﺍﺟﻊ-:
ﺍﻟﺒﺎﺏ ﺍﻷﻭﻝ
ﻭﻳﺸﻤﻞ ﻣﻘﺪﻣﺔ ﻋﻦ ﺍﻟﻤﺸﻜﻠﺔ ﻭﺍﻟﻬﺪﻑ ﻣﻦ ﺍﻟﺪﺭﺍﺳﺔ ﻭﺃﻫﻤﻴﺘﻬﺎ ،ﻧﻄﺎﻕ ﺍﻟﻌﻤﻞ ﻭﺍﻟﺨﻄﻮﺍﺕ ﺍﻟﻤﺘﺒﻌﺔ ﻓﻰ ﺍﻟﺪﺭﺍﺳﺔ ﻛﺬﻟﻚ ﻋﺮﺽ ﻟﻤﺤﺘﻮﻳﺎﺕ ﺍﻟﺒﺤﺚ.
ﺍﻟﺒﺎﺏ ﺍﻟﺜﺎﻧﻲ
ﻳﺼﻒ ﻫﺬﺍ ﺍﻟﻔﺼﻞ ﻣﻮﺍﺭﺩ ﺍﻟﻤﻴﺎﻩ ﻭﺇﺩﺍﺭﺓ ﺟﻮﺩﺓ ﺍﻟﻤﻴﺎﻩ ﻓﻰ ﻣﺼ�ﺮ ،ﺑﻤ�ﺎ ﻓ�ﻲ ﺫﻟ�ﻚ ﻣﺼ�ﺎﺩﺭﺗﻠﻮﺙ ﻧﻬ�ﺮ ﺍﻟﻨﻴﻞ ،ﺇﻟﻰ ﺟﺎﻧﺐ ﺷﺮﺡ ﻟﻤﻌﺎﻣﻼﺕ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴ�ﺎﻩ ،ﻭ ﻧﻤﺬﺟ�ﺔ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ ،ﻭﺍﺳ�ﺘﻌﺮﺍﺽ ﻟ�ﺒﻌﺾ ﺃﻣﺜﻠﺔ ﻟﻨﻤﺎﺫﺝ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﺍﻟﻤﻌﺮﻭﻓﺔ ،ﻭﺍﺳﺘﻌﺮﺍﺽ ﻟﻠﺘﻄﺒﻴﻘﺎﺕ ﻋﻠﻰ ﻧﻤﺎﺫﺝ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ ،ﻭﻛ�ﺬﺍﻟﻚ ﺇﺩﺍﺭﺓ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ ﺑﺎﺳ�ﺘﺨﺪﺍﻡ ﻧﻈ�ﻢ ﺍﻟﻤﻌﻠﻮﻣ�ﺎﺕ ﺍﻟﺠﻐﺮﺍﻓﻴ�ﺔ .ﺷ�ﺮﺡ ﻹﺳ�ﺘﺨﺪﺍﻣﺎﺕ ﻧﻈ�ﻢ ﺍﻟﻤﻌﻠﻮﻣ�ﺎﺕ ﺍﻟﺠﻐﺮﺍﻓﻴﺔ ﻓﻲ ﺇﺩﺍﺭﺓ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ،ﻭﻳﺸﻤﻞ ﻫ�ﺬﺍ ﺍﻟﺒ�ﺎﺏ ﺃﻳﻀ�ﺎ ﻋﻠ�ﻰ ﺗﻌﺮﻳ�ﻒ ﺑﺎﻷﺳﺎﺳ�ﻴﺎﺕ ﺍﻟﻤﻄﻠﻮﺑ�ﺔ ﺃﺧ��ﺬﻫﺎ ﻓ��ﻲ ﺍﻻﻋﺘﺒ��ﺎﺭ ﻋﻨ��ﺪ ﺇﻧﺸ��ﺎء ﻗﺎﻋ��ﺪﺓ ﺑﻴﺎﻧ��ﺎﺕ ﻹﺩﺍﺭﺓ ﻧﻮﻋﻴ��ﺔ ﺍﻟﻤﻴ��ﺎﻩ ﻣﻤ��ﺎ ﻳﺴ��ﺎﻋﺪ ﻓ��ﻰ ﺳ��ﻬﻮﻟﺔ ﺍﺳﺘﺮﺟﺎﻉ ﺍﻟﺒﻴﺎﻧﺎﺕ ﻭﻋﻤﻞ ﺗﺤﻠﻴﻞ ﻟﻬﺎ ﻣﻦ ﺧﻼﻝ ﻧﻤﻮﺫﺝ ﺍﻟﺘﻘﻴﻴﻢ ﻭﻧﻤﻮﺫﺝ ﺍﻟﻤﺆﺷ�ﺮ ﺍﻟﺘﻘﻴﻤ�ﻰ ﻳ�ﺘﻢ ﺗﻘﻴ�ﻴﻢ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﻭﻣﻘﺎﺭﻧﺘﻬﺎ ﺑﺎﻟﺤﺪﻭﺩ ﺍﻟﻤﺴﻤﻮﺡ ﺑﻬﺎ ،ﻭﺃﺧﻴ�ﺮﺍ ﻳ�ﺘﻢ ﺍﺳ�ﺘﻌﺮﺍﺽ ﺍﻟﺪﺭﺍﺳ�ﺎﺕ ﺍﻟﺴ�ﺎﺑﻘﺔ ﻓ�ﻲ ﻣﺠﺎﻝ ﺍﺩﺍﺭﺓ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﻭﻧﻤﺬﺟﺘﻬﺎ.
ﺍﻟﺒﺎﺏ ﺍﻟﺜﺎﻟﺚ
ﻳﺸ�ﻤﻞ ﻫ�ﺬﺍ ﺍﻟﺒ�ﺎﺏ ﻋﻠ�ﻰ ﺷ�ﺮﺡ ﺗﻔﺼ�ﻴﻠﻰ ﻋ�ﻦ ﺣﺎﻟ�ﺔ ﺍﻟﺪﺭﺍﺳ�ﺔ ،ﺑﻤ�ﺎ ﻓ�ﻲ ﺫﻟ�ﻚ ﺧﺼ�ﺎﺋﺺ ﺍﻟﻤﻴ�ﺎﻩ ﺍﻟﻬﻴﺪﺭﻭﻟﻮﺟﻴﺔ ﻭ ﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﻴﺔ ﺍﻣﺎﻡ ﻣﺤﻄﺎﺕ ﻣﻴ�ﺎﻩ ﺍﻟﺸ�ﺮﺏ ﺑﺎﻟﻘ�ﺎﻫﺮﺓ .ﻭﻳﺘﻀ�ﻤﻦ ﺃﻳﻀ�ﺎ ﺍﺳ�ﺘﻌﺮﺍﺽ ﻟﻤﺸﻜﻠﺔ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﺑﻤﻨﻄﻘﺔ ﺍﻟﺪﺭﺍﺳﺔ .ﻛﺬﺍﻟﻚ ﻛﻴﻔﻴﺔ ﺭﺻﺪ ﻭ ﻣﺘﺎﺑﻌﺔ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ،ﻭ ﺃﺧﻴﺮﺍ ﺗﻘﻴ�ﻴﻢ ﻧﻮﻋﻴﺔ ﺍﻟﻤﻴﺎﻩ ﻭﻣﺼﺎﺩﺭﺍﻟﺘﻠﻮﺙ ﺍﻟﻤﺨﺘﻠﻔﺔ ﺑﻤﻨﻄﻘﺔ ﺍﻟﺪﺭﺍﺳﺔ.
ﺍﻟﺒﺎﺏ ﺍﻟﺮﺍﺑﻊ
ﻳﺸﺘﻤﻞ ﻫﺬﺍ ﺍﻟﺒﺎﺏ ﻋﻠﻰ ﻭﺻﻒ ﻣ�ﻮﺟﺰ ﻟﻠﻨﻤ�ﻮﺫﺝ ﺍﻟﻤﺴ�ﺘﻨﺒﻂ ﻹﺩﺍﺭﺓ ﺟ�ﻮﺩﺓ ﺍﻟﻤﻴ�ﺎﻩ ﺍﻣ�ﺎﻡ ﻣﺤﻄ�ﺎﺕ ﻣﻴ�ﺎﻩ ﺍﻟﺸﺮﺏ ﺍﻟﻤﻮﺟﻮﺩﺓ ﻋﻠﻰ ﻧﻬﺮ ﺍﻟﻨﻴﻞ ﺇﻟﻰ ﺟﺎﻧﺐ ﺷﺮﺡ ﻟﻨﻈﺎﻡ ﺇﺩﺍﺭﺓ ﺍﻟﻤﻌﻠﻮﻣﺎﺕ ﺍﻟﺨﺎﺻﺔ ﺑﺠﻮﺩﺓ ﺍﻟﻤﻴﺎﻩ ). (WQMISﺛﻢ ﺷﺮﺡ ﺗﻔﺼﻴﻠﻰ ﻟﺒﺮﻧﺎﻣﺞ ﺍﻟﻤﺤﺎﻛﺎﺓ ﺍﻟﻤﺴﺘﺨﺪﻡ ﻓﻰ ﻫﺬﻩ ﺍﻟﺪﺭﺍﺳﺔ ).(MIKE11
ﺍﻟﺒﺎﺏ ﺍﻟﺨﺎﻣﺲ
ﻳﺸ�ﺘﻤﻞ ﻫ�ﺬﺍ ﺍﻟﺒ�ﺎﺏ ﻋﻠ�ﻰ ﺷ�ﺮﺡ ﻟﻠﻤﻨﻬﺠﻴ�ﺔ ﺍﻟﻤﺴ�ﺘﺨﺪﻣﺔ ﻓ�ﻲ ﺍﻟﺪﺭﺍﺳ�ﺔ ﻣ�ﻊ ﺍﻳﻀ�ﺎﺡ ﺗﻔﺎﺻ�ﻴﻞ ﺧﻄ�ﻮﺍﺕ ﺗﻄﺒﻴ�ﻖ ﻧﻤ�ﻮﺫﺝ ﺍﻟﻤﺤﺎﻛ�ﺎﺓ ،ﻭﻛ�ﺬﺍﻟﻚ ﻋﻤﻠﻴ�ﺔ ﺍﻟﻤﻌ�ﺎﻳﺮﺓ ﻭﺷ�ﺮﺡ ﺗﻔﺼ�ﻴﻠﻰ ﻟﺴ�ﻴﻨﺎﺭﻳﻮﻫﺎﺕ ﺍﻟ�ﺘﺤﻜﻢ ﺍﻟﻤﺨﺘﻠﻔ�ﺔ،ﻣﻊ ﺷ�ﺮﺡ ﻛﻴﻔﻴ�ﺔ ﺇﺟ�ﺮﺍء ﺍﻟﺘﻘﻴ�ﻴﻢ ﺍﻟﺸ�ﺎﻣﻞ ﻟﻬ�ﺬﻩ ﺍﻟﺴ�ﻴﻨﺎﺭﻳﻮﻫﺎﺕ ﺑﺎﺳ�ﺘﺨﺪﺍﻡ )،(MCAﻣ�ﻊ ﺍﺳﺘﻌﺮﺍﺽ ﻟﻠﻨﻤﻮﺫﺝ ﺍﻟﻤﺴﺘﻨﺒﻂ ﻹﺩﺍﺭﺓ ﺟﻮﺩﺓ ﺍﻟﻤﻴﺎﻩ ﺍﻣﺎﻡ ﻣﺤﻄ�ﺎﺕ ﻣﻴ�ﺎﻩ ﺍﻟﺸ�ﺮﺏ ﺑﺎﻟﻘ�ﺎﻫﺮﺓ ﺑﺈﺳ�ﺘﺨﺪﺍﻡ ﺑﺮﻧﺎﻣﺞ) (Microsoft Visual C
ﺍﻟﺒﺎﺏ ﺍﻟﺴﺎﺩﺱ
ﻳﻘﺪﻡ ﻣﻠﺨﺺ ﻟﻨﺘﺎﺋﺞ ﺍﻟﺒﺤﺚ ﻭﻛﺬﻟﻚ ﺍﻟﺘﻮﺻﻴﺎﺕ ﻟﻠﺪﺭﺍﺳﺎﺕ ﺍﻟﻤﺴﺘﻘﺒﻠﻴﺔ.
ﻗﺎﺋﻤﺔ ﺍﻟﻤﺮﺍﺟﻊ
ﺍﻟﻤﺮﺍﺟﻊ ﺍﻟﻤﺴﺘﻔﺎﺩ ﻣﻨﻬﺎ ﻓﻰ ﺍﻟﺒﺤﺚ ﻣﻊ ﺍﻟﺘﺮﺗﻴﺐ ﺍﻷﺑﺠﺪﻯ ﺍﻟﻤﻨﺎﺳﺐ.
ﺍﻟﻤﻼﺣﻖ
ﻭﻣﻦ ﺃﻫﻢ ﺍﻟﻨﺘﺎﺋﺞ ﻭﺍﻟﺘﻮﺻﻴﺎﺕ ﺍﻟﺘﻲ ﺗﻮﺻﻞ ﺇﻟﻴﻬﺎ ﺍﻟﺒﺤﺚ ﻫﻲ -:
-1ﻣﻄﺎﺑﻘ��ﺔ ﺗﺮﻛﻴ��ﺰﺍﺕ ﻋﻨﺎﺻ��ﺮ ﻧﻮﻋﻴ��ﺔ ﺍﻟﻤﻴ��ﺎﻩ ﺑﻤﻨﻄﻘ��ﺔ ﺍﻟﺪﺭﺍﺳ��ﺔ ) ﻟﻜ��ﻞ ﻣ��ﻦ ﺍﻻﺱ ﺍﻟﻬﻴ��ﺪﺭﻭﺟﻴﻨﻲ ،ﺍﻻﻛﺴ���ﺠﻴﻦ ﺍﻟ���ﺬﺍﺋﺐ ،ﺍﻻﻣ���ﻼﺡ ﺍﻟﺬﺍﺋﺒ���ﺔ ﺍﻟﻜﻠﻴ���ﺔ ،ﺍﻻﻛﺴ���ﺠﻴﻦ ﺍﻟﺤﻴ���ﻮﻱ ﺍﻟﻤﻤﺘﺺ،ﺍﻟﺤﺪﻳ���ﺪ،ﺍﻟﻨﺘﺮﺍﺕ، ﺍﻻﻣﻮﻧﻴﺎ( ﻟﻠﺤﺪﻭﺩ ﺍﻟﻤﺴﻤﻮﺡ ﺑﻬﺎ ﺑﺎﻟﻤﻮﺍﺻﻔﺎﺕ ﺍﻟﻘﻴﺎﺳﻴﺔ ﺍﻟﻤﺼﺮﻳﺔ ﺍﻟﻮﺍﺭﺩﺓ ﺑﻘﺎﻧﻮﻥ 48ﻟﺴﻨﺔ .1982 -2ﺑﻠ��ﻎ ﻣﺘﻮﺳ��ﻂ ﺗﺮﻛﻴ��ﺰ ﺍﻻﻛﺴ��ﺠﻴﻦ ﺍﻟﻤﻤ��ﺘﺺ ﻛﻴﻤﻴﺎﺋﻴ��ﺎ ﺑﻤﻴ��ﺎﻩ ﺍﻟﻨﻴ��ﻞ ﺍﻣ��ﺎﻡ ﻣﺤﻄ��ﺎﺕ ﻣﻴ��ﺎﻩ ﺍﻟﺸ��ﺮﺏ ﺑﺎﻟﻘ��ﺎﻫﺮﺓ 17.95±1.6ﻣﻠﺠ��ﻢ/ﻟﺘ��ﺮ ﻭﺍﻟ��ﺬﻱ ﻳﺘﺠ��ﺎﻭﺯ ﺍﻟﺤ��ﺪﻭﺩ ﺍﻟﻘﻴﺎﺳ��ﻴﺔ ﺍﻟ��ﻮﺍﺭﺩﺓ ﺑﻘ��ﺎﻧﻮﻥ 48ﻟﺴ��ﻨﺔ 10)1982ﻣﻠﺠ��ﻢ/ﻟﺘ��ﺮ( ،ﻭﻛ��ﺬﻟﻚ ﺑﻠ��ﻎ ﻣﺘﻮﺳ��ﻂ ﻋ��ﺪﺩ ﺍﻟﺒﻜﺘﺮﻳ��ﺎ ﺍﻟﻘﻮﻟﻮﻧﻴ��ﺔ ﺍﻟﺒﺮﺍﺯﻳ��ﺔ ﺑﻤﻴ��ﺎﻩ ﺍﻟﻨﻴ��ﻞ ﺍﻣ��ﺎﻡ ﻣﺤﻄ��ﺎﺕ ﻣﻴ��ﺎﻩ ﺍﻟﺸ��ﺮﺏ ﺑﺎﻟﻘ��ﺎﻫﺮﺓ 1387±18ﻭﺣ��ﺪﺓ ﻣﻴﻜﺮﻭﺑ��ﺔ ﻭﺍﻟ��ﺬﻱ ﻳﺘﺠ��ﺎﻭﺯ ﺍﻟﺤ��ﺪﻭﺩ ﺍﻟﻘﻴﺎﺳ��ﻴﺔ ﺍﻟﻮﺍﺭﺩﺓ ﺑﺎﻟﻤﻮﺍﺻﻔﺎﺕ ﺍﻟﻘﻴﺎﺳﻴﺔ ﻟﻤﻨﻈﻤﺔ ﺍﻟﺼﺤﺔ ﺍﻟﻌﺎﻟﻤﻴﺔ . (1000 CFU/100ml)1989 -3ﺗﺮﻭﺍﺡ ﻣﺘﻮﺳﻂ ﻣﺆﺷ�ﺮ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ ﺍﻟﺴ�ﻄﺤﻴﺔ )(WQIﺍﻣ�ﺎﻡ ﻣﺤﻄ�ﺎﺕ ﻣﻴ�ﺎﻩ ﺍﻟﺸ�ﺮﺏ ﺑﺎﻟﻘ�ﺎﻫﺮﺓ ﺑ��ﻴﻦ 91.22±1.17 %ﺍﻟ��ﻲ 97.22±1.42 %ﻭﺑﺘﺼ��ﻨﻴﻒ ﻳﺘ��ﺮﺍﻭﺡ ﺑ��ﻴﻦ ﺟﻴ��ﺪ ﺍﻟ��ﻲ ﻣﻤﺘ��ﺎﺯ ﻁﺒﻘ��ﺎ ﻟﺘﺼﻨﻴﻒ ﺍﻟﻤﻮﺍﺻﻔﺎﺕ ﺍﻟﻜﻨﺪﻳﺔ ).(CCME WQI -4ﻓﻲ ﺿﻮء ﻧﺘﺎﺋﺞ ﺍﻟﺴ�ﻴﻨﺎﺭﻳﻮﻫﺎﺕ ﺍﻟﻤﺨﺘﻠﻔ�ﺔ ﻟﻠﻤﺤﺎﻛ�ﺎﺓ ﺍﻟﻬﻴﺪﺭﻭﻟﻴﻜﻴ�ﺔ ﻟﻨﻬ�ﺮ ﺍﻟﻨﻴ�ﻞ ﺑﻤﻨﻄﻘ�ﺔ ﺍﻟﺪﺭﺍﺳ�ﺔ ﺑﺎﺳﺘﺨﺪﺍﻡ ﺑﺮﻧﺎﻣﺞ ) (MIKE11ﺗﺒﻴﻦ ﺍﻧﻪ ﻳﻤﻜﻦ ﺗﺤﺴﻴﻦ ﺟﻮﺩﺓ ﻣﻴ�ﺎﻩ ﻧﻬ�ﺮ ﺍﻟﻨﻴ�ﻞ ﺍﻣ�ﺎﻡ ﻣﺤﻄ�ﺎﺕ ﻣﻴ�ﺎﻩ ﺍﻟﺸﺮﺏ ﺑﺎﻟﻘﺎﻫﺮﺓ . -5ﻳﺘ�ﻴﺢ ﻧﻈ�ﺎﻡ ﺍﻟﻤﻌﻠﻮﻣ�ﺎﺕ ﺍﻟ�ﺬﻱ ﺗ�ﻢ ﺗﺼ�ﻤﻴﻤﻪ ﺑﺎﺳ�ﺘﺨﺪﺍﻡ ﺑﺮﻧ�ﺎﻣﺞ Microsoft Visual Cﻣ�ﻦ ﺧﻼﻝ ﺭﺑﻄﻪ ﺑﻜﻞ ﻣﻦ ﻧﻈﻢ ﺍﻟﻤﻌﻠﻮﻣﺎﺕ ﺍﻟﺠﻐﺮﺍﻓﻴﺔ) (GISﻭﻧﻤﻮﺫﺝ ﺍﻟﻤﺤﺎﻛ�ﺎﺓ ﺑﺒﺮﻧ�ﺎﻣﺞ )(MIKE11 ﻋﺮﺽ ﻭﺍﺳﺘﺮﺟﺎﻉ ﻭ ﺗﺤﻠﻴﻞ ﺍﻟﺒﻴﺎﻧ�ﺎﺕ ﻭﺍﻟﺴ�ﻴﻨﺎﺭﻳﻮﻫﺎﺕ ﺍﻟﻼﺯﻣ�ﺔ ﻹﺩﺍﺭﺓ ﻧﻮﻋﻴ�ﺔ ﺍﻟﻤﻴ�ﺎﻩ ﺃﻣ�ﺎﻡ ﻣﺤﻄ�ﺎﺕ ﻣﻴﺎﻩ ﺍﻟﺸﺮﺏ ﺑﺎﻟﻘﺎﻫﺮﺓ .
Table of Contents Page i ii iii viii ix x
Acknowledgement …………………………………………… Abstract ……………………………………………………… Table of contents……………………………………………. List of Figures ………………………………………………... List of Tables…………………………………………………. List of Abbreviations ………………………………………… Chapter (1) : Introduction 1-1General ……………………………………………………. 1 1-2 Problem Statement ……………………………………….. 2 1-3 Study Objectives ………………………………………….. 2 1-4 Methodology …………………………………………….. 2 1-6 Thesis Outline …………………………………………….. 3 Chapter (2) : Literature Review 2-1 Introduction ………………………………………………. 5 2-2 Egypt Water Resources and Availability…………………. 5 2-3 Water Resources Management …………………………...
6
2-4 Water Uses ……………………………………………
7
2-5 Challenges in the Water Sector …………………………..
7
2-6 Water Quality Management ……………………………
8
2-6-1 Water Quality Index …………………………………. 2-6-2 Background on River Nile Water Quality Status ……. 2-6-3 Legislative Aspects …………………………………..
8 9 10
2-7 River Nile System Pollution Sources …………………….
10
2-8 Water Quality Monitoring………………………………..
11
2-9 Water Quality Parameters ………………………………..
12
iii
2-9-1 Physico-Chemical Parameters ………………………… 2-9-2 Major Anions …………………………………………. 2-9-3 Trace Metals…………………………………………… 2-9-4 Microbiological Parameters ………………………….. 2-10 Water Quality Modeling …………………………………. 2-10-1 Mathematical Models Grouping…………………........
Page 12 15 16 16 17 17
2-11 Water Quality Management Using GIS…………………… 2-12 Water Quality Management Using Multi Criteria Analysis ………………………………………………………. 2-13 Water Quality Management Information System ………… Chapter (3): Study Area
18
3-1 The Study Area Characteristics …………………………….
23
3-1-1 Hydrological Characteristics …………………………… 3-1-2 Hydraulic Gauging Stations ……………………………. 3-2 Cairo Drinking Water Plants ………………………………..
23 24
3-3 Cairo Surface Water Quality ……………………………….. 3-3-1 Hydraulic Gauging Stations …………………………….
25
3-4 Laboratory Analysis ………………………………………… Chapter (4): Methodology …………………………………….
26 28
0B
19 20 21
24 25
4-1 Water Quality Index Determination ……………………….. 28 4-2 Study Area Data in GIS ……………………………………. 29 4-3 Considerations behind Modeling Study Area ……………… 30 4-4 MIKE11 Model Water Quality Model …………………….. 4-4-1 Selection of MIKE11 model in this study…………… 4-4-2 The Conceptual Model……………………………….
v
31 31 31
4-4-3 Processes of the Model ............................................... 4-4-4 MIKE11 Model Formation......................................... 4-4-4-1 The Simulation Editor ..........................................
Page 32 34 34
4-4-4-2 The Simulation Network Editor……………………
35
4-4-4-3 The Cross-Section Editor………………………… 4-4-4-4 The Boundary Editor.............................................. 4-4-4-5 The Hydrodynamic Parameter Editor..................... 4-3-4-6 The Advection-Dispersion Editor………………...... 4-4-4-7 WQ ECO LAB Editor 4-4-4-8 Mike VIEW……………………………………….. 4-4-5 Model Calibration …………………………………… 4-4-6 Model Run…………………………………………… 4-4-7 Model Validation…………………………………….. 4-4-8 Model Evaluation Statistics………………………….. 4-4-8-1 Relative Mean Absolute Error……………………. 4-4-8-2 Percent Bias (PBIAS)…………………………….. 4-4-8-3 Nash-Sutcliffe Efficiency (NSE)……… …………. 4-4-8-4 Coefficient of Determination …….…… …………. 4-5 Applying MCA Technique…………………………………. 4-5-1 MCA Formation………………………………….. 4-6 Water Quality Management Information System …………. 4-6-1 Database Structure Design……………………………
35 36 36 36 37 37 37 37 37 38 38 38 39 39 39 41 41 42 42
4-6-2 Information System………………………………….. 4-6-3 Developing Graphical User Interface (GUI)………….
42
Chapter (5): Results and Discussion
45
5-1 Water Quality Assessment …………………………………
45
5-2 Study Area Water Quality Modeling……………………… 5-2-1 Model Calibration…………………………………….. 5-2-2 Model Run ……………………………………………
49 49 51
5-2-2-1 Modeling of Dissolved Oxygen……………………
51
vi
5-2-2-2 Modeling of Biochemical Oxygen Demand……… 5-2-2-3 Modeling of Chemical Oxygen Demand…………. 5-2-2-4 Modeling of Fecal Coliform………………………… 5-2-3 Model Evaluation Statistics …………………………. 5-2-4 Model Validation and Testing……………………….. 5-3 Water Quality Management Scenarios……………………. 5-3-1 Scenario (1) Treatment of Four Polluted Drains Using Wetland Technique……………………………………………. 5-3-2 Scenario (2) Treatment of drinking water plant sludge disposal ……………………………………………………….. 5-3-3 Scenario (3) Increasing the Study reach flow up to 20 percent over the maximum discharge in low demand period…… 5-3-4 Scenario (4) Increasing study reach flow, treatment of polluted drains using wetland technique and treatment of drinking water plant sludge disposal…………………………. 5-3-5 Scenario (5) Treatment of Four Polluted Drains by constructing wastewater treatment plants …………………. 5-3-6 Scenario (6) Increasing study reach flow, treatment of polluted drains by constructing wastewater treatment plants and treatment of drinking water plant sludge disposal …………….. 5-4 Scenarios Evaluation……………………………………….. 5-5 Cairo Reach Water Quality Management Information System…………………………………………………………… 5-5-1 Cairo Reach Module…………………………………… 5-5-2 Pollution Sources Module…………………………….. 5-5-3 Water Quality Data Module……………………………. 5-5-4 Modeling Results Module……………………………… 5-5-5 Reports Module…………………………………………
Page 53 55 57 59 60 62 63 64 64
65 66
67 68 71 71
72 72 73 73 75 Chapter (6) : Conclusion and Recommendations 6-1 Conclusions…………………………………………….. 76 6-2 Recommendations …………………………………………. 77 References ……………………………………………….. ……. 79 Appendixes………………………………………………………. 91
vii
List of Figures Page Figure (2-1) Figure (2-2) Figure (3-1) Figure (3-2) Figure (4-1) Figure (4-2) Figure (4-3) Figure (4-4) Figure (4-5) Figure (4-6) Figure (5-1a) Figure (5-1b) Figure (5-1c) Figure (5-2a) Figure (5-2b) Figure (5-2c) Figure (5-3a) Figure (5-3b) Figure (5-3c) Figure (5-4a) Figure (5-4b) Figure (5-4c) Figure (5-5a) Figure (5-5b) Figure (5-5c) Figure (5-6) Figure (5-7)
Nile Research Institute Water Quality Monitoring Network Subdivisions of Water-Quality Models in Common Use Study Area Layout Discharge River Nile at Cairo Reach downstream Assyut Barrage and Upstream Delta Barrage
MIKE 11Different Complexity Levels The Simulation Editor Linkage in MIKE11 Model Input Tab of Simulation Editor in MIKE 11 Model Cross-Section Editor of MIKE11 Model MCA Main Criteria and Indicators Cairo Drinking Water Plants Quality Management Model Mean Annual Simulated Salinity, 2012 GIS Map for Simulated and Observed EC (μS/cm),2012 Relation between Observed and Simulated Mean Annual EC(μS/cm), 2012 Mean Annual Simulated DO Profile, 2013 GIS Map for Simulated and Observed DO Relation between Observed and Simulated DO, 2013 Mean Annual Simulated BOD Profile,2013 GIS Map for Simulated and Observed BOD, 2013 Simulated and Observed BOD Relation, 2013 Mean Annual Simulated COD Profile, 2013 GIS Map for Simulated and Observed COD Mean Annual Simulated Salinity, 2012 GIS Map for Simulated and Observed Salinity,2012 GIS Map for Mean Annual Simulated Fecal Coliform, 2013 Simulated and Observed FC Relation, 2013 GIS Map for Mean Annual DO, 2014 GIS Map for Mean Annual BOD,2104
viii
12 17 22 23 32 34 35 36 50 44 50 50 51 52 52 53 54 54 55 56 56 57 57 58 58 60 60
Figure (5-8) Figure (5-9) Figure (5-10) Figure (5-11) Figure (5-12) Figure (5-13) Figure (5-14) Figure (5-15) Figure (5-16) Figure (5-17) Figure (5-18)
GIS Map for Mean Annual COD, 2014 GIS Map for Mean Annual FC,2104 Sludge Disposal Alternatives Summary of Water Quality Improvement under Different Scenarios MCA Total Weight Scores WQMIS Main Interface Cairo Reach Module Pollution Sources Module Water Quality Module Modeling Results Module Reports Module
viii
page 61 61 66 68 70 71 71 72 72 73 74
List of Tables Table (2-1) Table (3-1) Table (3-2) Table (3-3) Table (4-1) Table (4-2) Table (4-3) Table (5-1) Table (5-2) Table (5-3) Table (5-4) Table (5-5) Table (5-6) Table (5-7) Table (5-8) Table (5-9) Table (5-10) Table (5-11) Table (5-12) Table (5-13) Table (5-14)
Some of Water Quality Indices Summary Gauging stations along the study area Annual total average raw water, treated water and Sludge & washing water for Cairo drinking water plants. Pollution Source Locations Water Quality Index Classification Study Area Graphical Data Study Area Attribute Data Spatial variation of surface water quality parameters and WQI Model Evaluation Statistics Model Validation Statistics Management Scenarios Description The Expected Performance of Wetland System Water Quality Improvement under Scenario(1) Water Quality Improvement under Scenario(2) Water Quality Improvement under Scenario(3) Water Quality Improvement under Scenario(4) The Expected waste water treatment plants typical removal rates Water Quality Improvement under Scenario(5) Water Quality Improvement under Scenario(6) Water quality improvement under various scenarios MCA for Management Scenarios Evaluation viii
ix
List of Abbreviations
Page 9 24
25 26 29 30 30 46 59 62 62 63 63 64 65 65 66 66 67 68 69
Abbreviations AD BOD CCME
CT COD
DO DHI DRI DSS DWP EC EEAA EPA FC GIS HAD HD HSPF NEQS MCA MHUNC MWRI NBOD NWRC SOD TC TDS TMDLs USEPA VBA WASP WHO WQ WQI WQD WQP
Referent Advection-dispersion Biological Oxygen Demand Canadian Council of Ministers of the Environment Disinfection Contact Time Chemical Oxygen Demand Dissolved Oxygen Danish Hydraulic Institute Drainage Research Institute Decision Support System Drinking Water Plant Electrical Conductivity Egyptian Environmental Affairs Agency Environmental Protection Agency Fecal Coliform Geographic Information System High Aswan Dam Hydrodynamic Hydrological Simulation Program {FORTRAN} National Environmental Quality Multi Criteria Analysis Ministry of Housing, Utilities and New Communities Ministry of Irrigation and Water Resources Nitrogenous Biochemical Oxygen Demand National Water Research Center Sediment Oxygen Demand Total Coliform Total Dissolved Solids Total Maximum Daily Loads US Environmental Protection Agency Visual Basic for Application Water Quality Analysis Simulation Program World Health Organization Water Quality Water Quality Index Water Quality Data Water Quality Parameters x
List of Symbols
pH CO3-2 HCO3CaCO3 NH3 Ca2+ Mg2+ Na+ 0C ClNO2NO3PO4-3 NH4–N SO4-2 Mn Zn Cu Al Cd Cr Fe Hg Ni μS/cm
A measure of the activity of the hydrogen ion Carbonates Bicarbonates Calcium Carbonate Ammonia Calcium Magnesium Sodium Degrees Centigrade Chloride Nitrite Nitrate Phosphate Ammonium Nitrogen Sulfate Manganese Zinc Copper Aluminum Cadmium Chromium Iron Mercury Nickel Micro Siemens per centimeter
xi
Chapter (1) Introduction 1-1Genral Water is the most important natural resource not only for a state or a country, but also for the entire humanity. The prosperity of a nation depends primarily upon the judicious exploitation of this resource. Thus, it can be stated that the primary wealth of a nation is water which flows in rivers and streams. This establishes the importance of rivers, and no other explanation is required to stress their importance. River basin, as a domain for planning and management has been accepted the world over, as water does not recognize political boundaries. A wide range of human activities may lead to environmental deterioration of surface and ground water resources, either directly or indirectly. Deteriorating water quality is a particular threat in countries with scare water resources. Conventional water resources in Egypt are the Nile water, rainfall, groundwater, and desalinated water. The Nile water supplies are extremely limited by the 55.5 billion cubic meters at High Aswan Dam (HAD), and projected to become even more limited due to the increased competition on water resources among the Nile basin countries. Any reduction in flow of the Nile River would put additional stress on water resources throughout Egypt. Annual water share of Nile water per capita in Egypt has decreased from 2500 m3/capita/yr in the 1950s to about 680 m3/capita/yr in 2012, and is projected to drop to about 350 m3/capita/yr in 2050 (MWRI, 2005). As far as Egypt is concerned, adequate supplies of fresh water is critical to the long term, for water resources management focused on reallocating water to when and where it was required, a supply-side approach. In recent years, it has become increasingly apparent that the quality of available water is as important as the quantity. Poor water quality can render available supplies unsuitable for its intended uses. Water quality in the Nile deteriorates along the course of the river. Lake Nasser has good water quality with only small organic substance concentrations below the national guidelines, which makes its water a reference point for water quality along the river and its branches. In 2013, according the Egyptian Environmental Affairs Agency (EEAA) reports, average organic loads of 11 governorates along the Nile remained below the allowed limit of 6 mg/l of biological oxygen demand (BOD). This is due to the high selfassimilation capacity of the Nile. However, in the same year chemical oxygen demand was above the allowed. Thus, water quality, if not adequately managed, can serve as a serious limiting factor to the future
1
economic development , public health and the environment which will result in enormous long term costs to the society. Therefore, the need for better management of the quality of surface water resources is greatly recognized and a particular attention of surface water quality upstream Cairo drinking water plants become high priority of concern.
1-2 Problem Statement Sustainable development of water resources in Egypt is impeded by a number of challenges, most notably a rapid population growth with the limited resources and water scarcity. This imposes insufficient utilities and wastewater treatment facilities which causes water quality deterioration especially in rural areas. The government put on its shoulder an ambitious plan to cover the rural area with utilities and wastewater treatment plants . Surface water quality deterioration at the intakes of Cairo water treatment plants along River Nile due to increasing level of some pollutants concentration above the guidelines paid the attention of public concern and may cause health hazards. Thus, the need for better management of Cairo treatment plants water sources quality is becoming essential. .
1-3 Study Objectives The main objective of the research is managing surface water quality upstream Cairo water drinking plants. This main objective can be divided into the following specific objectives:1-Estimate the surface Water Quality Index (WQI) upstream Cairo drinking water plants along River Nile. 2-Derive the best surface water quality modeling approach for the study area. 3-Develop and evaluate the management scenarios for improving water quality. 4-Develop the Water Quality Management Information System (WQMIS) to facilitate water quality management upstream Cairo drinking water plants.
2
1-4 Methodology Figure (1-1) describes the schematic diagram of the work methodology.
Figure(1-1) Study methodology Schematic Diagram
1-6 Thesis Outlines This thesis is divided into six chapters which can be summarized as follows:-
Chapter (1): Introduction This chapter gives main overview on problem statement, research objective, methodology, scope of work and thesis outlines.
Chapter (2): Literature Review This chapter describes the water resources and water quality management in Egypt including River Nile pollution sources; this includes description of water quality parameters and Water Quality Index. In addition, water quality modeling, review of examples of some known models. Review the literature on applications of water quality
3
models, review literature on the uses of GIS in water quality management and looking for the previous research.
Chapter (3): Study Area This chapter includes a general background information about the case study (Cairo water drinking plants along Nile River), including river hydrology, hydraulic characteristics of the study area. It also describes a water quality problem, water quality monitoring of the study area.
Chapter (4): Methodology This chapter deals with the methodology adopted for the research, the work tasks in the study, which can be divided into six parts. The first, determination of the existing surface water quality index (WQI); the second, modeling surface water quality upstream Cairo drinking water plants using MIKE11 the third, creating and evaluation the management scenarios by Multi Criteria Analysis (MCA); the fourth, developing Water Quality Management Information System(WQMIS) for the study area.
Chapter (5): Results & Discussions In this chapter, assessment of water quality and pollution sources in the study area is explained. MIKE11 program for water quality and hydraulic modeling are explained in details, besides calibration, verification and validation processes. Then the results of Multi Criteria Analysis (MCA) for evaluation and ranking various water quality management scenarios are discussed. In the last part of this chapter, the description of the developed WQMIS and their different modules is explained.
Chapter (6): Conclusion & Recommendations This chapter presents the conclusions and recommendations of this study in order to be used for further research.
4
Chapter (2) Literature Review 2-1 Introduction This chapter describes the water resources and water quality management in Egypt, including Nile River pollution sources with the description of water quality parameters, water quality modeling, water quality management using GIS and WQMIS.
2-2 Egypt Water Resources and Availability Egypt is an arid country covering an area of approximately 1 million km2, of which its population occupies only 5.5% of the total area. The availability of fresh water resources in the country is limited mainly to the Nile River, groundwater from both renewable and non-renewable aquifers, limited rainfalls along the northern coast and flash floods in the Sinai Peninsula,(MWRI, 2003). Egypt also practices the use of various types of marginal quality water, such as agricultural drainage water, treated domestic wastewater and desalinated brackish water. Egypt receives about 98% of its fresh water from the Nile, originating outside its international borders. This is considered a major challenge for Egyptian water policy and decision makers. The average annual yield of the river is estimated at 84 BCM at Aswan, south of Egypt. However, Egypt's share from the Nile is fixed at 55.5 BCM per year by the 1959 agreement with Sudan, (MWRI, 2003). Groundwater is an important source of fresh water in Egypt, both within the Nile system and in the desert. The renewable groundwater aquifer of the Nile system is recharged from excess irrigation water as well as leakages from the Nile and the distribution network. Current abstraction from the Nile aquifer is about 4.8 BCM/yr and is expected to reach 7.5 BCM/yr by the year 2017, (El-Sayed, 2011) . Groundwater also exists in the non-renewable deep aquifers in the Western Desert and Sinai. The total extraction potential of groundwater is estimated at 3.5 BCM/yr (Abdo,2010). Rainfall is very scarce and occurs only during the winter season in the form of scattered showers with a total amount that may reach 1.5 BCM/yr. Therefore, rainfall cannot be considered a dependable source of water, (MWRI, 2003).
5
Agricultural drainage water has emerged as the most attractive type of unconventional resource in Egypt in supplementing available water resources. Indeed, reuse of agricultural drainage water has been adopted as a national policy since the late 1970s. Currently, an amount of 5 BCM/yr of drainage water in the Nile Delta is reused directly or after mixing with fresh water. Interest in the use of treated wastewater, as a substitute for fresh water in irrigation, has accelerated since 1980. Currently, 0.7 BCM/yr of treated wastewater is being used in irrigation, of which 0.26 BCM is undergoing secondary treatment and 0.44 BCM undergoing primary treatment. Desalination had low priority due to its high cost. Nevertheless, it is being used to produce and supply drinking water for some locations along the Mediterranean and Red Sea coasts, (Abdel-Gawad S., 2009).
2-3 Water Resources Management Given the importance of water for the socio-economic development of the country, the government of Egypt is committed to take all necessary means and measures to manage and develop the water resources of the country in a comprehensive and equitable manner. The growing population and economic activities have increased the pressure on the water system, both with respect to quantity as quality of the water. Consequently, the annual per capita share of renewable water resources (mainly provided by the Nile) is dramatically reduced from more than 2500 cubic meters at the year 1950 to less than 900 cubic meters at the year 2000, and is further projected to fall to about 500 m3/cap/yr by the year 2050, (MWRI, 2010). The challenges facing the water sector in Egypt are enormous and require the mobilization of all resources and the management of these resources in an integrated manner. It is believed that business-as-usual scenarios are no longer adequate in meeting the current challenges. Changes in the way water resources are currently allocated and managed are inevitable. Accordingly, the prevailing water scarcity, Egypt has endorsed several policies to achieve both integration and decentralization of water management to the lowest possible level. Ministry of Water Resource and Irrigation is implementing the Strategy of Water Resources Management 2050 to fulfill the later objectives including the establishment of water user associations, the transfer towards integrated water management districts, and matching irrigation demands systems (MWRI, 2013). The main specific strategy objectives are:-
6
• Supply of drinking water for domestic uses and the provision of sanitation services, according to the standards, on a cost recovery basis but taking into account the right on basic requirements of all people. • Supply of water for industrial purposes and the provision of sewage treatment facilities. • Supply of water for irrigation based on a participatory approach and cost recovery of operation and maintenance. • Protection of the water system from pollution, based on a polluter- pays principle and the restoration of water systems, in particular the ecological valuable areas.
2-4 Water Uses The prime water consumer in Egypt is the agricultural sector, with its share exceeding 82% of the total gross demand for water. The total water diverted to agriculture from all sources that includes conveyance, distribution and application losses is estimated to be about 60 BCM/yr. Municipal water demands including water supply for major urban areas and rural villages is currently in the order of 5.5 BCM/yr. Industrial water demand is estimated at 7.5 BCM/yr. A small portion of this quantity (0.8 BCM) is consumed through evaporation during industrial processes, while the rest returns back to the system in a polluted form. Water supply and sanitation is managed by the Ministry of Housing, Utilities and New Communities (MHUNC). The Ministry of Water Resources and Irrigation (MWRI) is responsible for ensuring water of an acceptable quality for all sectors, (Abdel-Gawad S., 2009).
2-5 Challenges in the Water Sector There are six main challenges facing water management in Egypt. The first and most important challenge is the growing population and the related increased water demand for public water supplies and economic activities. The second challenge is the expected Nile flow reduction which has emerged recently due to the rapid implementation plans of the Ethiopian Dams that might happen during the storage of dam. Thus the support of Sudanese side is very important to reduce the potential threats of the dam. The third challenge stems from the expected impacts of climate change on the Nile flows and the different demands of the water sector. The fourth challenge is the water quality in the canals' network due to the interaction with the domestic, industrial and agricultural activities of the increased population, in particular in the Nile Delta.
7
The fifth challenge is the institutional setting of water management which is a governmental one and central by nature. The management of the water sector should be effective and able to deal with the recent rapid expected changes. Finally, the sixth one is sea level rise that is threatening the coastal zones and the Nile Delta in particular, where a significant area is subject to inundation as well as impacting the quality of the coastal fresh water aquifers due to sea water intrusion.
2-6 Water Quality Management The purpose of water quality management is to achieve sustainable use of our water resources by protecting and enhancing their quality while maintaining economic and social development. Water quality management involves the identification and assessment of point and non-point source pollutants and their sources, and then determining the best management practices to control those pollutants to meet water quality standards, (Abou El- Ftouh, 2013). 2-6-1 Water Quality Index The Water Quality Index (WQI) is one of the most effective tools to monitor the surface as well as ground water pollution and can be used efficiently in the implementation of water quality upgrading programmers. The objective of an index is to turn multifaceted water quality data into simple information that is comprehensible and useable by decision makers and the public. WQI used in many countries such as the United States of America, Canada, Spain, France, Germany, Austria, Italy, Poland and Turkey. Rita et al (2011) made a study on WQI seasonal variation of Sabarmati River at Ahmed abed, Gujarat, India. The results of their study revealed that the quality of Sabarmati River was adversely affected by discharge of domestic, agricultural and industrial effluents as a result of extended urbanization. Almost all water quality indices depends upon normalizing, data parameter by parameter according to expected concentrations and interpretation of ‘good’ versus ‘bad’ concentrations. Then parameters are weighted according to their perceived importance to overall water quality and the index is calculated as the weighted average of all observations of interest. Table (2-1) Summaries some of most common water quality indices.
8
Table (2-1) Some of Water Quality Indices Summary Index
Objective
Method
Environmental Performance Index
Environment al health and ecosystem vitality
Chemical Water Quality Index
Lake basin
The Scatter score index
Water quality
Index of River Water Quality
River health
Uses a proximity-to-target measure for sixteen indices categorized into six policy objectives Assesses a number of water quality parameters by standardizing each observation to the maximum concentration for each parameter Assesses increases or decreases in parameters over time and space Uses multiplicative aggregate function of standardized scores for a number of water quality parameters Assessment and classification of a number of water quality parameters by comparing observations against Indian standards and/or other accepted guidelines e.g. WHO Assesses quality of water against guidelines for freshwater life
Overall Index of Pollution
River health
Water Quality Index for Freshwater Life
Inland waters
The Well-being of Nations
Human and Ecosystem
Assesses human indices against ecosystem indices
Author Levy et al. (2006)
Tsegaye et al. (2006) Kim and Cardone (2005) Liou et al. (2004)
Sargaonkar and Deshpande (2003) CCME (2001) PrescottAllen (2001)
2-6-2 Background on River Nile Water Quality Status The water quality of the main stream of the River Nile from Aswan to Cairo can be considered good with a little of various pollutants trace. However, water quality in the irrigation and drainage canals deteriorates downstream and reaches alarming levels in the Delta (Abd El-Daiem, 2011). As the River Nile flows downstream from High Aswan Dam (HAD), the total salt load increase while the volume of water decreases because of additional drainage water and the continuous abstraction of water used for different purposes (Abdo, et. al., 2010). The water quality in the Nile downstream from Aswan has changed dramatically as the Nile water became silt-free, less turbid and with considerably less velocity (Saad and Goma, 1994).
9
2-6-3 Legislative Aspects Egypt has no one general law on integrated water resource management, but several laws and decrees in three main sectors: Water resources (most of them on the Nile River waterways), Environment and water protection, Wastewater management and wastewater reuse. The main laws concerning the water resources and their protection (including wastewater discharge) are:• •
• •
Law 27/1978 for the regulation of water resources and treatment of water. Law 48/1982 with its ministerial decree 92/2013 by the Ministry of Water Resources and Irrigation regarding the protection of the River Nile and waterways from pollution. This law is the main law for controlling the discharge of wastewater to the river and waterways. Specific laws for irrigation, law 12/1984 and law 213/1994, define the use and management of public and private sector irrigation and drainage systems. Concerning the protection of the environment, including water resources, there is: an environmental law 4/1994 and its ministerial decree 92/2013, amended by law 9/2009 that governs environmental protection in Egypt. The laws define environmental impact assessment (three categories: A, B, C).
•
Presidential decree 3318/2009 created the Supreme Council for Protection of River Nile and Waterways from Pollution. Ministries with water management responsibilities participate in this Council.
•
Few laws concern directly the wastewater management (except, for instance, law 93/1962 on discharge of wastewaters to sewerage networks, amended by decrees 649/1962, 9/1989 and 44/2000), but numerous decrees regulate the wastewater sector:Decree 169/1997 Egyptian Code for wastewater treatment works.
•
2-7 River Nile System Pollution Sources Water pollution is considered to be one of the most dangerous hazards affecting Egypt. Pollution in the Nile River System (main stem Nile, drains and canals) has increased in the past few decades because of increases in population with insufficient utilities and wastewater treatment; several new irrigated agriculture projects, and other activities along the Nile. As the program to expand irrigated agriculture moves
10
forward, the dilution capacity of the Nile River system will diminish at the same time that the growth in industrial capacity is likely to increase the volume of pollutants discharged to the Nile. The severity of present water quality problems in Egypt varies among different water bodies depending on: flow, use pattern, population density, extent of industrialization, availability of sanitation systems and the social and economic conditions existing in the area of the water source. Discharge of untreated or partially treated industrial and domestic wastewater, leaching of pesticides and residues of fertilizers; and navigation are often factors that affect the quality of water.
2-8 Water Quality Monitoring Under the hierarchy organizational of National Water Research Center (NWRC), National Water Research Institute (NRI) is intensively involved in the monitoring of water quality of the River Nile and its branches. All the water quality parameters are to be measured according to the updated edition of the American Standard Methods for Examination of Water and Wastewater. The main objectives of the surface water quality monitoring network are:- Assess the water quality entering Egypt and released from High Aswan Dam (HAD) monitoring Lake Nasser. - Monitor the seasonal variation of the water quality along the Nile and irrigation canals. - Quantify the variation in the drainage water quality in relation to the existing different pollution sources. - Identify the quality and quantity of drainage water reuse in agriculture. NRI operate and maintain a database related to water quality monitoring network which consists of 43 agricultural drain discharges to the Nile between Aswan and Delta Barrage, 43 Nile River monitoring stations and 12 Irrigation stations. The monitoring schedule is to sample the network twice per year, once during the high and the second during the low flow periods. Figure (21) presents a graphical depiction of the NRI monitoring network (Radwan, M. and El-Sadek, A., 2005).
11
Figure (2-1) Nile Research Institute Water Quality Monitoring Network
2-9 Water Quality Parameters 2-9-1 Physico-Chemical Parameters • pH
The pH is an important variable in water quality assessment as it influences many biological and chemical processes within a water body
12
and all processes associated with water supply and treatment,. The pH of most natural Nile River water at Egypt varies between 6.0 to 8.5, (Abou El- Ftouh, 2013). In drinking water plants, the impact of pH on chlorine disinfection has been demonstrated in the field. Virus inactivation studies had shown that 50 percent more contact time is required at pH 7.0 than at pH 6.0 to achieve comparable levels of inactivation. These studies also demonstrated that a rise in pH from 7.0 to 8.8 or 9.0 requires six times the contact time to achieve the same level of virus inactivation, (Aragones, 2009). • Total Alkalinity It is a measure of the ability of your water to resist changes in pH, which would tend to make the water more acidic. It is important that there is a good balance to the alkalinity of water. If the alkalinity is too low, the ability of your water to resist pH changes decreases. This means that the pH will vibrate up and down, changing from acidic to basic fairly rapidly. Water with low alkalinity can also be corrosive and can irritate the eyes, (El-Sayed, S., 2011). Water with high alkalinity has a soda-like taste, can dry out skin and can cause scaling on fixtures and throughout water distribution systems. This scaling is undesirable because it begins to decrease the efficiency of drinking water plants, which results in greater power consumption and increased costs. However, many public drinking water utilities try to maintain an acceptable alkalinity level in order to prevent low pH (acidic) water from damaging pipelines and other distribution equipment. • Total
Dissolved Solids (TDS) It is a measure of the combined content of all inorganic and organic substances contained in a liquid in molecular, ionized or micro-granular suspended form, (Moustafa, 2010). The United States has established a secondary water quality standard of 500 mg/l to provide for palatability of drinking water. High concentrations of TDS limit the suitability of water as a drinking and livestock watering source as well as irrigation supply. High TDS waters may also interfere with the clarity, color, and taste of drinking water. • Electrical
Conductivity (EC) It is a measure of the ability of water to conduct an electric current. It is sensitive to variations in dissolved solids, mostly mineral salts. Conductivity is expressed as micro Siemens per centimeter (μS/cm). Conductivity often is used to estimate the amount of total dissolved solids (TDS) rather than measuring each dissolved constituent separately
13
• Ammonia
NH3 It is a reduced form of nitrogen (NH4+) and together with the nonionized form (NH3) they compose ammonia. Ammonia is frequently present in groundwater sources where there is no oxygen present. Ammonia ions play a key part in water treatment because they need to be removed before breakpoint chlorination can be achieved. Breakpoint needs to be reached to comply with primary disinfection requirements. The use of chlorine for ammonia removal can only be recommended for water sources with less than 1 mg/L of ammonia (preferably less than 0.2 mg/L). If breakpoint chlorination is not achieved then the water treatment plant is using what is called secondary disinfection, which should only be used after primary disinfection has been carried out, (NAWQAM, 2003). • Biochemical
Oxygen Demand (BOD) It is an approximate measure of the amount of biochemically degradable organic matter present in a water sample. It is defined by the amount of oxygen required for the aerobic micro-organisms present in the sample to oxidize the organic matter to a stable inorganic form, (Abdo, M.H, 2010). BOD can also be used for evaluation the efficiency of treatment process and it is indirect measure of biodegradable organic compounds in water. • Chemical Oxygen Demand (COD) It is another measure of organic material contamination in water specified in mg/L. COD is the amount of dissolved oxygen required to cause chemical oxidation of the organic material in water. Both BOD and COD are the key indicators of the environmental health of a surface water supply. They are commonly used in wastewater treatment but rarely in general water treatment, (Saravanakumar, K. and R. Ranjith, Kumar, 2011). • Total Organic Carbon (TOC) It is an indicator of the quality of water as a source of drinking water. Organic carbon and bromide are precursors to the formation of harmful disinfection byproducts (DBPs) in municipal water supplies. Water source with high dissolved organic carbon (DOC) and bromide concentrations requires additional treatment steps, increases the cost of treatment, and may lead to increased health risk from exposure to disinfection byproducts.
14
2-9-2 Major Anions • Nitrite
(NO2) It is a one measure form of nitrogen that occurs as an intermediate in the nitrogen cycle. It is an unstable form that is either rapidly oxidized to nitrate (nitrification) or reduced to nitrogen gas (de-nitrification). This form of nitrogen can also be used as a source of nutrients for plants. Since nitrite is also a source of nutrients for plants its presence encourages plant proliferation. Nitrite is toxic to aquatic life at relatively low concentrations, (Mabrouk, D.R. 2004). • Nitrate (NO3) It is an indicator for the leaching of nutrients from agriculture and treatment plants. Because the nitrate ion is stable and highly soluble it is not amenable to removal by conventional water treatment processes such as coagulation and precipitation or adsorption and filtration. Specialized processes have to be used involving catalysts, high temperature and pressure. A wide variety of chemical reducing agents have been used to reduce nitrates but not all are suitable for the treatment of drinking water. • Phosphate
( PO4) It is an essential nutrient for living organisms. It exists in water bodies as dissolved and particulate. Phosphorous and phosphates trigger algal blooms that deplete the receiving waters of oxygen under certain conditions, killing the aquatic life,(Abdel-satar,2010). Algal blooms impact the source water quality for drinking water utilities. Treatment technologies currently available for phosphorus removal include those categorized as physical (e.g., filtration and membrane technologies), chemical (e.g., precipitation and physical-chemical adsorption), biological (e.g., assimilation and enhanced biological phosphorus removal).Physical treatment with sand filtration is similar to a sand filter in a drinking water plant. Membrane technology is also being employed and can include micro-, nano, ultra-filtration or reverse osmosis. Sulfate (SO4) It is naturally present in surface waters as SO4. Industrial discharges and atmospheric precipitation can also add significant amounts of sulphate to surface waters. Sulphate concentrations in natural waters are usually between 2 and 80 mg/l, although they may exceed 1,000 mg/l near industrial discharges or in arid regions where sulphate minerals, such as gypsum, are present. Drinking water with excess sulphate concentrations often has a bitter taste and a strong ‘rotten-egg’ odour, (Sabae, S.Z, 2004). Sulphate can also interfere with disinfection efficiency by scavenging residual chlorine in distribution systems. Sulphate salts are 15
capable of increasing corrosion on metal pipes in the delivery system and sulphate-reducing bacteria may produce hydrogen sulphate which can give the water an unpleasant odour and taste and may increase corrosion of metal and concrete pipes. 2-9-3 Trace Metals The ability of a water body to support aquatic life, as well as its suitability for other uses, depends on many trace elements. Many of traces metals discharged into natural waters at increased concentrations in sewage, industrial effluents or from mining operations can have severe toxicological effects on humans and the aquatic ecosystem. The assessment of metal pollution is an important aspect of most water quality assessment programs, (EL-Naggar, A.M.; Mahmoud, S.A., 2009).The Global Environment Monitoring System (GEMS) programs includes ten metals: Al, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn. Water pollution by heavy metals as a result of human activities is causing serious problems to drinking water plants such as scales, corrosion, precipitatation in filtration units, unpleasant taste & odor and excessive treatment chemical consumption, (Abou El- Ftouh, 2013). 2-9-4 Microbiological Parameters The microbiological parameters are related to human health and are influenced through human and agricultural wastes. In drinking water plants, the most popular microbiological indicators for water source pollution are total and fecal coliform (Saravanakumar, K. and R. Ranjith, Kumar, 2011). The coliform bacterial count has been the most frequently used bacterial test. Fecal coliform bacteria are a group of bacteria that are passed through the fecal excrement of humans, livestock and wildlife. The bacteria can be found in the digestive tract of warm-blooded animals and aid in the digestion of food. Fecal coliform bacteria do not pose a danger to people or animals; however, where fecal coliform are present, disease-causing bacteria may also be present. Fecal coliform contamination may indicate that water is polluted with human or animal waste, which can harbor other pathogens that may threaten human health, (Daboor, S. M., 2006). E-Coli are one type of pathogenic fecal coliform bacteria, and the most common facultative, disease-causing bacteria in the feces of warmblooded animals. Most E. coli bacteria are harmless and are found in great quantities in the intestines of people and warm-blooded animals. Some strains, however, can cause illness. In drinking water plants, disinfection process including disinfectant type, disinfection doses and contact time affected by existence of potential water source disease.
16
2-10 Water Quality Modeling One of the important aspects of the water quality management is to predict the change of water quality resulting from natural phenomena, from changes in land and water use and from human activities. Such prediction is an essential tool for decision makers. This prediction can be done by determining trends and extrapolating them in the future or by mathematical models. Water quality models are designed to simulate the responses of aquatic system under varying conditions. They have been applied to explain and predict the effects of human activates on water resources, such as the lake eutrophication, dissolved oxygen concentration in rivers and the impacts of toxic substances on the freshwater system,(Chapman,1996). 2-10-1 Mathematical Models Grouping Figure (2-2) shows the classification of common water quality models. This classification is based on water body, purpose of the model, spatial characteristics, temporal characteristics, and types of data used.
Figure (2-2) Subdivisions of Water-Quality Models in Common Use.
17
Abou El-Ftouh., (2013) prepares a water quality model for Rosetta Branch zones in the northern part of Egypt by using WASP7. This model was calibrated and used to simulate different scenarios to solve the water quality problems in this Nile River branch. The WASP7 model results and its different simulation scenarios could facilitate assessing, predicting water pollution in this branch and they can also provide easier process for decision making to decrease the water pollution. Bhatti. M (2009) studies spatial and temporal water quality of river chenab in Pakistan, a mathematical model (MIKE11 model developed by Danish Hydraulic Institute (DHI), Denmark) was formulated to simulate a conservative water quality parameter (salinity of river water). The calibrated model for salinity simulated the most saline condition in the river during the months with minimum flow. The results of simulations indicated DO depletion and high BOD levels in the downstream river. The study of management scenarios for BOD suggested that the maximum water quality improvement can be achieved if there is no diversion of flow from the river coupled with 60 percent reduction in BOD of the drain effluents through treatment. Kamal et al. (1999), applied MIKE11 for modeling water quality status of Buriganga River in Bangladesh. The study included some cardinal parameters for water quality assessment out of which DO was modeled using MIKE 11 model. Simulated results of the model showed very low DO level in the river. Through the series of alternative scenario simulations with the calibrated water quality model, it was found that discontinuation of any of the major point sources of pollutants might not be adequate to improve minimum DO level in river Buriganga. It was also noted that a dramatic improvement of the minimum DO level in the river was observed if all the major pollutant sources were treated for biodegradable material and disposed at a location further downstream of its existing point of entry into the river. 2-11 Water Quality Management Using GIS GIS are capable of combining large volumes of data from a variety of sources, they are a useful tool for many aspects of water quality investigations. They can be used to identify and to determine the spatial extent and causes of water quality problems, such as the effects of landuse practices on adjacent water bodies. Zeynel Demirel (2007) studied the water quality management of Berdan river basin in Turkey. The management of such a system which contains a lot of parameters can be succeeded by using GIS. A GIS was
18
constructed and numerical and thematic maps are produced for Berdan basin by Map Info Water quality model was constructed by WQM-Cal. Jon Goodall and Tim Whiteaker (2003) Studied Bacteria transport to Galveston Bay, Texas, to estimate the transport of bacteria from nonpoint sources to the Galveston Bay from a GIS environment with Arc Toolbox 9.0 technology. Schematic Network Processing was used to link the land, river, and bay systems. This entire process was implemented using Model Builder allowing the processes to be linked and implemented with just one click. White and Hofschen (1993) developed a spatial model for assessing nutrient loads in New Jersey River using Arc/Info. They used 3 arc-sec digital elevation models (DEM) to partition the study area .They also constructed a GIS model of total phosphorus concentrations in New Jersey streams. The core of this model was a regression equation that related transformed (natural logarithm) total phosphorus concentration measured at a given point to transformed concentrations resulting from exponentially decayed phosphorous loads in the upstream 2-12 Water Quality Management Using Multi Criteria Analysis(MCA) The common purpose of MCA methods is to evaluate and choose among alternatives based on multiple criteria using systematic analysis that overcomes the limitations of unstructured individual or group decisionmaking. MCA methods employs numerical scores to communicate the merit of each option on a single scale. Scores are developed from the performance of alternatives with respect to individual criteria and then aggregated into an overall score. Individual scores may be simply summed or averaged, or a weighting mechanism can be used to favor some criteria more heavily than others. The goal of MCA is to find the optimum option for decision-makers’ preferences. The primary components in the structure of any MCA must reference and define generic performance criteria together with appropriate supporting multicriteria decision-making parameters. Such multi-criteria approaches must be flexible and dynamic in order that they can be adapted and reviewed to meet changing circumstances and constraints within regulations and customers. The application of multi-criteria analysis should also incorporate a risk and sensitivity assessment stage in terms of determining which options are more likely to be the more sustainable under uncertain and variable conditions. In addition to being used in the remediation of aquatic ecosystems, MCA techniques have been used in attempts to reduce of the amount of pollution entering those ecosystems.
19
Doley et al. (2001) use MCA to find an optimal way to reduce nitrogen discharge into the Potomac River. They couple a water quality model with an optimization model to assess the best way to reduce nitrogen discharges from various land use types. 2-13 Water Quality Management Information System (WQMIS) Water Quality Management Information System (WQMIS) is designed to integrate all the pollutant relevant information into one single data structure, which facilitates performing water quality analyses. The purpose of the software is to simplify the complicated relationships for the sources of pollution and surface streams to allow decision-makers to evaluate the implication of policy related water quality control measures through standards limits, (Farag, 2004). WQMIS integrates the water quality data, water quality modeling and water quality analysis modules using:• A database in which the key elements of WQMIS. The database is linked to Graphical Information System (GIS) software for visualization the information and highlighting the potential location of pollution problem. The Water Quality Index (WQI) is constructed to help in water quality analysis process. • A simulation model which is capable of simulating water quality pollutant transport, which helps in analyzing and predicting the effect of discharging the pollutants; • An interface, which is designed to cover most, the functional of the operations in water quality management process. The Geographic User Interface (GUI) linked to Graphical Information System (GIS) software to be flexible in visualization the in presenting the formation.
20
Chapter (3) Study Area The Nile River is considered as one of the longest river in the world. Its length is approximately 6740 Km. It flows from south at Ethiobia plateau to Egypt. The Nile River enters Egypt at its southern boundary with Sudan and runs through 1000 km long narrow valley, then divided at a distance of 25 km north of Cairo into two branches (Rosetta and Damietta) forming a delta resting with its base on the Mediterranean Sea. Cairo, located on the Nile River south of the Mediterranean Sea, just upstream of the point where the river widens into the Delta. Cairo has an area of 353 km2 with an average reach length along the river about 50 km (from Km 900 to km 950 Referenced to Aswan High Dam). The study area covers Cairo governorate along the River Nile, bounded by El Saff town at Km 877.00 from the South and El Kanater town at Km 953.00 from the North. Figure (3-1) illustrates the study area layout.
21
Figure (3-1) Study Area Layout
3-1 The Study Area Characteristics 3-1-1 Hydrological Characteristics Generally, the hydrological regime of the Nile River is characterized by low and high discharge rates. The high discharge corresponds to the rains and low discharge corresponds to the dry season in the catchments' zones, especially in Ethiopian plateau. Groundwater in the Nile valley is
22
an important resource for both irrigation and domestic water supply, particularly for villages. The Nile alluvium is in direct hydraulic contact with the Nile River. It is replenished from the Nile River, by excess irrigation water, seepage from the irrigation distribution system and from drains (Attia, 1992). Study area has a width along Nile River varies from 400 m to 700 m, an average depth varies from 6 m to 9 m and a mild climate with maximum temperature throughout the entire year ranges from 18oC to 36oC. Rainfall has an average intensity about 20 mm/year, (Berg, 1981). The high flow conditions are during June, July, and August. A relatively stable low flow conditions are found through the rest of the year. The average monthly discharge of Nile River at Cairo reach, downstream Asyut barrage and upstream Delta barrage during the period from 1980 to 2010 is illustrated in Figure (3-2). 2000 1800
1400 1200 1000 800 600 400 200
Oc to be r No ve mb er De ce mb er
be r
Se pt em
Month
Au gu st
Ju ly
Ju ne
ay M
Ap ri l
ar ch M
y Fe br au ry
0
Ja nu ar
Discharge (m3/s)
1600
Figure (3-2) Discharge River Nile at Cairo Reach downstream Assyut Barrage and Upstream Delta Barrage (Abou El-Ftouh., 2013)
3-1-2 Hydraulic Gauging Stations Study area hydrological data are obtained from three main gauging stations. Each gauging station has a historical record since 1955 (Rasslan and Abd El bary, 2001). Table (3-1) illustrates the locations of the distributed gauging stations along the study area.
23
Table (3-1) Gauging stations along the study area (Rasslan and Abd El bary, 2001) Serial Drinking waterNo. plant 1 Tibeen
2 3
Plant intake Surface Station Name geographicEkhsase water location El Roda source Cairo River Nile
Delta Barrage
Treated Kilometer from water HAD Raw water production (m3/day) 887.00 (m3/day) 927.20 178608 155649
953.00
Sludge and washing water (m3/day) 47000
3-2 Cairo Drinking Water Plants Cairo water company(CWC), a subsidiary of the Holding Company of Water and Wastewater, produces potable water with a amount reaches to 6 million m3/day used by inhabitants of Greater Cairo, (CWC, Annual Report, 2014). This is done through 13 Cairo drinking water plants (Tibeen, Kafr Elw, North Helwan, Maadi, Fostat, El Roda, Rod El Farg, Amerea, Mostrod, El Marg, El Obour, El Asher, Shubra el Khiema) distributed in Greater Cairo. Most of The Greater Cairo dirking water plants discharge the sludge into the Nile River again without treatment at all. The sludge is composed of the impurities removed and precipitated from the raw water together with the residuals of any treatment chemical used, (CWC, Central laboratory annual technical report, 2014). The discharging of sludge into water body leads to accumulative rise of aluminum concentrations in water, aquatic organisms, and human bodies. Some researchers have linked aluminum concentrations in the human bodies Alzheimer’s disease, children mental retardation, and the common effects of heavy metals accumulation (Prakhar and Arup 1998), so disposal of alum sludge can be a major concern for Cairo drinking water plants. Table (3-2) shows the annual average raw water, treated water and sludge & washing water for Greater Cairo drinking water plants, (CWC, Central laboratory annual technical report, 2014).
Table (3-2) Annual average raw water, treated water and sludge & washing water for Greater Cairo drinking water plants,(CWC, 2014).
24
Kafr Elw North Helwan Maadi Fostat El Roda Rod El Farg Amerea Mostrod El Marg El Obour El Asher Shubra el Khiema Gezeret El Dahab 6 October Giza Embaba
Cairo
Ismailia canal
New Cities Qalubia
Sharkawia canal
River Nile Giza
El Sheikh Zayed
El Rayah El Behery
78238 321003 209179 1114381 323216 819695 404226 1281328 650000 860000 600000
70728 283539 161772 1046974 164625 720908 389853 1155899 526232 790000 500000
14000 39000 86000 150000 7000 115000 25000 130000 41000 49000 39000
379146
358091
21582
550000
49000
30000
300000 168000 950000
275000 130000 922000
19000 9500 52000
500000
400000
42000
From table (3-2) and according to the study scope which focus on drinking water plants located on Cairo governorate along Nile River, however the study water quality management methodology will be applied on seven CDWPs (Tibeen, Kafr Elw, North Helwan, Maadi, Fostat, El Roda and Rod Farg) and taking into consideration the effect of pollution sources from Giza governorate drinking water plants which discharge their sludge into the Nile River (Gezeret El Dahab, Giza and Embaba DWPs only).
3-3 Cairo Surface Water Quality 3-3-1 Pollution Sources Cairo receives relatively high concentrations of organic compounds, nutrients and oil & grease. Study Reach Pollution sources can be divided to the following main types:1) Agricultural drainage water mixed with partially treated or untreated domestic wastewater and industrial wastewater: Massanda, Ghamaza Soghra and Ghamaza Kobra Drain. 2) Industrial wastewater effluent: Tibeen Power Station, El Nasser Glass and Delta Cotton Kanater. 3) Wastewater from drinking water plants sludge disposal: Tibeen, Kafr El Elw, North Helwan, Maadi, Fostat, Gezeret El Dahab, El Roda, Giza, Embaba Rod El Farag DWPs. 4) Mixed Waste: Khour Sail El Tibeen.
25
Table (3-3) illustrates the study area pollution sources (referenced to HAD). Table (3-3) Pollution Source Locations Pollution Source Name
Description
1 2
Distance from HAD (Km) 879.60 884.50
El Massanda Drain Ghamaza Soghra Drain
3
888.95
Ghamaza Kobra Drain
4
898.10
Khour Sail El Tibeen
5
901.10
Tibeen Power Station
Agricultural drainage water mixed with partially treated or untreated domestic wastewater and industrial wastewater Mixed Waste Waste and cooling water
6 7 8 9
907.50 910.50 914.00 922.00
Tibeen Drinking Water Plant Kafr El Elw Drinking Water Plant North Helwan Drinking Water Plant Maadi Drinking Water Plant
10
923.60
Fostat Drinking Water Plant
11
925.10
Gezeret El Dahab Drinking Water Plant
12
927.20
El Roda Drinking Water Plant
13
930.40
Giza Drinking Water Plant
14
933.00
Rod El Farag Drinking Water Plant
15
939.60
El Nasser Glass
16
942.30
Embaba Drinking Water Plant
17
947.90
Delta Cotton Kanater
No.
Wastewater from drinking water plants sludge disposal
Industrial effluent Wastewater from drinking water plants sludge disposal Industrial effluent
3-4 Laboratory Analysis Surface Water samples were collected from various sampling locations of rivers, canal, drains and industrial pollution sources of the study area. The water samples were collected from 57 locations including 4 locations for drains, 3 locations for industrial pollution sources and 10 locations for wastewater from drinking water plants sludge disposal.
26
Generally, the determination of sampling sites locations is mainly based on taking three sample at each pollution source location: the first, along the river and just before pollution source, the second at the end of pollution source before discharging into river to determine its effluent characteristics, the third, along the river at 200 meter after the pollution source to ensure a complete mixing of pollution water source discharge with river water, (Fischer, et al.,1979). All the samples water quality parameters were measured at Cairo drinking water company central laboratory.
Chapter (4) Methodology
27
This chapter describes the work tasks in the study which can be divided into four main parts. The first; determination of the existing surface water quality index (WQI), the second, modeling surface water quality upstream Cairo drinking water plants using MIKE11, the third, creating and evaluation the management scenarios, the fourth, developing the proposed Water Quality Management Information System (WQMIS) for the study area.
4-1 Water Quality Index Determination The desired WQI for study area was calculated based on Canadian Council of Ministers of the Environment (CCME, 2005). In the formulation of WQI, Nine physical, chemical and biological parameters: pH, Dissolved Oxygen (DO), Total Dissolved Salts (TDS), Nitrates ,Ammonia, Iron, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Fecal Coliform(FC) are selected according to their relative importance from the point of view of suitability for drinking water purposes. The analyses of water samples were carried out at Cairo drinking water company central laboratory according to the standard methods for the examination of water and wastewater (APHA, 2012) for twelve consequence months during year 2013 to show the effect of the spatial and temporal variation. The methodology of WQI determination is based on three measures of variance from selected water quality objectives (Scope; Frequency; Amplitude). These three measures of variance combine to produce a value between 0 and 100 that represents the overall water quality. The CCME WQI values are then converted into rankings by using an index categorization schema that can be customized to reflect expert opinion by users. The detailed formulation of the WQI is described in the Canadian water quality index , (CCME, 2005). The observed values of samples were compared with standard values recommended by Egyptian National water quality standards (objectives), Law 48/1982 regarding the protection of the River Nile and waterways from pollution Based on the collected water quality data set for year 2013, WQI values is calculated and then rated according to their corresponding ranks (excellent, good, fair, marginal and poor) as shown in table (4-1).
Table (4-1) Water Quality Index Classification, (CCME, 2005) Rank
WQI Value
Excellent
95-100
Description Water quality is protected with a virtual absence of threat or impairment; conditions 28
Good
80-94
Fair
65-79
Marginal
45-64
Poor
0-44
very close to natural or pristine levels; these index values can only be obtained if all measurements are virtually within objectives all of the time. Water quality is protected with only a minor degree of threat or impairment; conditions rarely depart from natural or desirable levels. Water quality is usually protected but occasionally threatened or impaired; conditions sometimes depart from natural or desirable Levels. Water quality is frequently threatened or impaired; conditions often depart from natural or desirable levels. Water quality is almost always threatened or impaired; conditions usually depart from natural or desirable levels.
4-2 Study Area Data in GIS Study Area data in GIS is divided into four main elements representing the overall entire databases:• The stream database • Pollution Sources database • Water Quality database • Simulation model database To utilize GIS, characteristics of the study area needed to be digitally represented in a format compatible with the GIS software, Arc/Info 10.0.The data used in a GIS system normally consist of two parts: graphical data and attribute data. In this research, vector data (Table 4-2) were used to graphically represent the water resources and water quality related objects of the study area. Furthermore, a number of attribute data (Table 4-3), mainly including the attributive descriptions of water resources, water pollution sources and water quality of the study area were collected. These data were added in the GIS and thematic maps were produced.
Table (4-2) Study Area Graphical Data Element Basic Maps
Contents River Drains
Configuration Line Line
29
Thematic Maps
Governorates Towns Water Quality Measuring Stations Drinking Water Stations Factories River Nile Segments
Polygon Polygon Point Point Point Polygon
Table (4-3) Study Area Attribute Data Element
Contents Drain
Pollution Data
Industry Water Quality Segment Characteristics
Model Data
Segment Water Quality Data Point Source Water Quality Data Model Output Data
Configuration Length, Width of Discharge, etc.. Factory number, type, volume of waste water discharge COD, BOD, DO, Temp., NH3, FC, etc.. Length, Depth, Width ,Discharge, etc.. COD ,BOD, DO, Temp., etc.. BOD, DO, FC, etc.. Different Scenarios
4-3 Considerations behind Modeling Study Area The application of hydraulic models in river studies provides a valuable insight into the dynamics of the fluvial system at a variety of scales. Their main functions include the duplication, examination and investigation of known flow events / phenomena, with resultant predictions of likely scenarios (Przedwojski et al., 1995). However, the success of computational hydraulic models relies upon the choice of an appropriate model (Warwick & Heim, 1995), together with the selection of suitable cross sections and their spacing (Samuels, 1990). A balance must be struck between having frequent sections and precise hydraulic representation, and the time and money spent on human and computational resources. Too much data can afford unnecessary expense in its collection, processing and simulation, whereas too little data can yield calculation instabilities and large errors (Samuels, 1990).
4-4 MIKE11 Water Quality Model This model is developed by the Danish Hydraulic Institute (DHI),2014. It is a powerful tool for modeling conditions in rivers, lakes, reservoirs, irrigation canals and other inland water systems. MIKE11 consists of
30
correlated modules that allow users to specify the type of hydrologic process. 4-4-1 Selection of MIKE11 Model for this Study The model was selected for this study for the following reasons:1-The MIKE11 model provides a graphical user interface and requires a river network to be drawn on a grid (mesh) of cells that represent geographical area to a referenced scale and /or coordinates. 2-It includes several options for water quality simulation and it Includes graphical post-processor with Geographic Information Systems interface. 3- High-speed eutrophication and organic chemical model processors. 4- Successfully applied to several rivers and lakes. 4-4-2 The Conceptual Model MIKE11 model is a 1D model for simulation flow and rivers water quality. The hydrodynamic module (HD) simulates dynamic flows and can be applied to branched and looped networks. The advection–dispersion module (AD) module can simulate first-order decays of determinants. The water quality processes include modeling of DO and BOD with nutrients, COD with nutrients, heavy metals, ironoxidation and nutrient transport. The component which involves the modeling of DO and BOD corresponds to different levels of increasing complexity as shown in Figure (4-1).
31
Figure (4-1) MIKE11 Different Complexity Levels
4-4-3 Processes of the Model The HD module is the core of the system and solves either the full hydrodynamic (St. Venant) equations or one of the two simpler versions called diffusive wave and kinematics wave equation. Writing equations for the conservation of mass and momentum separately results in the pair of equations called the Saint Venant equation:-
The St. Venant equation for momentum and how the simpler forms may be derived by dropping terms which are shown as follows (Chow, et al., 1988):
Where:A: is the wetted area (or reach volume per unit length). t: is the time. Q: is the discharge. x: is the distance downstream.
32
q: is the lateral inflow per unit length, g is the acceleration due to gravity. Y: is the depth, a: is a momentum coefficient. S0: is the bed slope Sf: is the friction slope. The AD module describes the basic processes of river water quality in areas influenced by human activities, e.g. oxygen depletion and BOD levels as a result of organic matter loads. Concentrations of DO and BOD are calculated in MIKE11 by taking into consideration advection, dispersion and the most important biological, chemical and physical processes. A predefined set of water quality (WQ) components is available in AD module which deals with the basic aspects of river water quality. The WQ component is coupled to the AD module, which means that the WQ module deals with the chemical/biological transforming processes of compounds in the river and the AD module is used to simulate the simultaneous transport process. The relevant water quality components must be defined in the AD editor. The onedimensional (vertically and laterally integrated) equation for the conservation of mass:-
Where:Q: is the discharge. C: is the concentration (arbitrary unit). D: is the dispersion coefficient. A: is the cross-sectional area. K: is the linear decay coefficient. C2: is the source/sink concentration Q: is the lateral inflow. x: is the space co-ordinate. t: is the time co-ordinate.
4-4-4 MIKE11 Model Formation The input requirements of the model e.g. river network, cross-sections, boundary data, HD, AD and Eco-Lab parameters were set up for the model runs, for different months as well under different management
33
scenarios. The modeling process in MIKE11 was completed by providing related data and information in different editors. The following is the description of different phases of the model formulation and the editors in MIKE11 modeling system:4-4-4-1 The Simulation Editor The simulation editor is the main editor in MIKE11 and the corresponding file should always be the first file that is created when initiating a new project. The simulation editor serves three purposes:1-It contains the simulation and computation control parameters. 2-It is used to start the simulation. 3-It provides a link between network editor and the other MIKE11 editors. Figure (4-2) the linkage between simulation editor and the main other MIKE11 editors.
Figure (4-2) The Simulation Editor Linkage in MIKE11 Model.
The linkage requires a file name to be specified for each of the required editors. This editor consists of five tabs i.e. model, input, simulation, results and start tabs. The ‘model’ tab contains a list of all modules supported by MIKE11 e.g. hydrodynamic, advection-dispersion, sediment transport, etc. The ‘input’ tab compiles all input files in the simulation editor (Figure 4-3). The input data files are edited in separate editors which are briefly discussed next.
34
Figure (4-3) The Input Tab of Simulation Editor in MIKE11 Model.
4-4-4-2 The Simulation Network Editor In this editor, the entire network of Nile River starting from El Saff town at Km 877 (referenced to HAD) till to Delta Barrage, Us Delta Barrage at Km 953 was drawn. All the river alignments were digitized according to the chainage (location point along the river) from the referenced plans used in the background of editor screen. 4-4-4-3 The Cross-Section Editor The cross section editor manages, stores, and displays all model cross sections information. There are two types of cross section data; the raw survey data and the derived processed data. The raw data describes the shape of the cross section and typically comes from a section survey of the river. The processed data is derived from the raw data and contains all information used by the computer model (e.g. level, cross section area, flow width, hydraulic/resistance radius). The processed data can be calculated by the cross section editor or entered manually. Figure (4-4) shows the cross-section setting.
35
Cross distance (m)
Figure (4-4) Cross-Section Editor of MIKE11 Model
4-4-4-4 The Boundary Editor This editor is the most important part of the modeling system. The boundary editor is used to specify boundary conditions of MIKE11 model. 4-4-4-5 The Hydrodynamic Parameter Editor (HD Editor) The HD-editor is used for setting supplementary data for simulation. Most of the parameters in this editor have default values and in most cases these values are sufficient for obtaining satisfactory simulation results. In the formulation of model for the present study, initial conditions of water depth and discharge were provided at different chainage of the river. 4-3-4-6 The Advection-Dispersion Editor (AD Editor) The AD-editor of MIKE11 model consists of seven Main tabs: Components, Dispersion, Initial Condition, Decay, Additional Output, Sediment Layers, Cohesive Sediment Transport, Non-Cohesive Sediment transport. This study included the modeling of COD, salinity, first order DO and BOD decay alternately. However, DO and BOD are defined in level 1 as indicated in Figure(4-1).
36
4-4-4-7 Water Quality ECO LAB Editor It is a numerical lab for ecological modeling. It is an open and generic tool for customizing aquatic ecosystem models to describe water quality, eutrophication, heavy metals and ecology. This module describes chemical, biological, and ecological processes and interactions between state variables and also the physical process of sedimentation of components can be described. 4-4-4-8 MIKE VIEW MIKE VIEW is a Windows application for displaying simulation results from MIKE11. The important features of MIKE VIEW are:• • • •
Results can be displayed and animated on horizontal plans. Series from external sources. Q-H relations can be displayed for selected locations. Time series of results can be copied to the other applications.
4-4-5 Model Calibration MIKE11 model was calibrated using water quality data for year 2012. Salinity was chosen for calibration process because it is considered a conservative material and it is an excellent water mass tracer. The calibration was done at various points along the study reach by comparing observed EC (μS/cm) with the measured data collected during year 2012. The calibration process was done by setting the initial EC values for the study area boundaries (El Saff and Delta Barrage) and also at pollution point sources (drains, factories and wastewater from DWPs) to their average values and setting other various study reach EC values to be zero . 4-4-6 Model Run After calibration of MIKE11 model with set of data for year 2012 , another set of water quality data for year 2013 was used for model run. 4-4-7 Model Validation The calibrated model with 2013 water quality data was validated using 2014 water quality data set. The simulated DO, BOD, COD and FC profiles are compared to evaluate the reliability of the model.
4-4-8 Model Evaluation Statistics The evaluation statistics were also applied to evaluate the reliability of model validation. The following statistical measures were applied to
37
these results to quantify the model accuracy and to estimate the errors in the simulated results. These statistical measures along with their interpretation criteria are briefly discussed as follows:4-4-8-1 Relative Mean Absolute Error (MAE) The mean absolute error (MEA) is an error index commonly used in model evaluation. It is computed as:-
The relative mean absolute error is calculated by dividing the MAE by the mean of observed values of the parameter being evaluated as shown in the equation:Where:X = Observed value of the parameter being evaluated Y= Simulated value of the parameter being evaluated n = total number of observations i = numbers 1, 2, 3, …….n The value of (MAE)rel equal to zero is considered optimal. 4-4-8-2 Percent Bias (PBIAS) Percent bias (PBIAS) measures the average tendency of the simulated data to be larger or smaller than their observed counterparts. The optimal value of PBIAS is zero, with low-magnitude values indicating accurate model simulation.
Positive values indicate model underestimation bias, and negative values indicate model overestimation bias (Gupta et al., 1999). PBIAS is calculated as: Where PBIAS is the deviation of data being evaluated, expressed as a percentage. Moriasi et al. (2007) has suggested the use of this statistical measure on the basis of following reasons: (1) PBIAS is calculated in the similar manner as that of percent deviation which is recommended by ASCE (1993) for model evaluation and (2) PBIAS has the ability to clearly indicate poor model performance (Gupta et al., 1999). 4-4-8-3 Nash-Sutcliffe Efficiency (NSE)
38
It is a normalized statistic that determines the relative magnitude of the residual variance compared to the measured data variance (“information”) (Nash and Sutcliffe, 1970). NSE is computed as:
Where NSE = Nash-Sutcliffe efficiency NSE ranges between −∞ and 1.0 (1 inclusive). The value of NSE equal to 1.0 is the optimal value. Values between zero and 1.0 are generally viewed as acceptable levels of performance, whereas values less than zero indicate that the mean observed value is a better predictor than the simulated value, which depicts unacceptable performance of the model. According to Moriasi et al. (2007), NSE is recommended for model evaluation for two major reasons:(1) it is recommended for use by ASCE (1993), Legates and McCabe (1999). (2) It is very commonly used, which provides extensive information on reported values. 4-4-8-4 Coefficient of Determination (R2) It describes the degree of co-linearity between simulated and observed data. R2 describes the proportion of the variance in measured data explained by the model. The value of R2 ranges from zero to 1. A high value of R2 indicates less error in variance, and typically values greater than 0.5 are considered acceptable (Santhi et al., 2001, Van Liew et al., 2003). The coefficient of determination is computed as:-
2
Where R = Coefficient of determination 4-5 Applying MCA Technique MCA identifies multiple criteria against which the study area water quality management scenarios can be evaluated and then compared to each other. MCA technique mainly based on ranking for prioritizing the alternatives through technical, economical environmental and sociocultural criteria, Figure (4-5) shows the main MCA criteria and indicators. Applying MCA technique in this study to offer several further advantages (Belton, 2002):-
39
• •
They offer more explicit reflection on value judgments concerning the alternatives. The decision process is structured. This promotes systematic thinking, definition of options, identification of criteria and impact assessment with respect to the various actors involved.
•
MCA criteria can also be cross-referenced to other sources of information on relative values, and amended if necessary.
•
Criteria that any decision making group may make are open to analysis and to change if they are felt to be inappropriate. Scores and weights are explicit and are developed according to established techniques. Performance measurement can be sub-contracted to experts, so need not necessarily be left in the hands of the decision making body itself It can provide an important means of communication, within the decision making body and sometimes, later, between that body and the wider community. Scores and weights are used, it provides an audit trail.
• • • •
Figure (4-5) MCA Main Criteria and Indicators,( Rosén et al. , 2009) 4-5-1 MCA Formation MCA scoring system is formed based on the procedure developed by the USEPA (Heaney et al., 1997) which scores all positive aspects of each 40
system type from 1 (lowest) up to 5 (highest having the most desirable conditions). The following methodological steps were followed to construct MCA:• Determine available water quality management scenarios "Discrete decision options" which usually will be ranked or scored. • Choose evaluation criteria. The criteria are used to measure the performance of decision options. • Obtain performance measures for the evaluation. These values be sourced from expert judgments and other environmental models. • Weight the criteria based on the degree of importance of each adaptation option. • Rank or score the options. At this stage the weights are combined with the performance measures to attain an overall performance rank or score for each decision option. • Prioritization of options based on the final weighted scores per option which calculated according to the equation:Where:Value (x) = Final value for alternative x Wi (x) = Weight of criterion i for alternative x Ci(x) = Score of criterion i for alternative x In this study, all parameters were weighted equally (weighting factor =6%) with the exception of the most important and effective four criteria relating to the Sustainability, Resource use, Cost of loss investments and Health- safety risks. These four criteria were allocated a weighting factor of 10% each. The scores and group rankings are based on information and data gathered from the international literature (Linkov (2006), Burgman, M. (2005), Goodwin & Wright, 2009; Lai et al., 2008) and also on personal experience. 4-6 Water Quality Management Information System (WQMIS) The WQMIS is developed based on building a comprehensive database system. The database enables storage, retrieval, querying and presentation of information. WQMIS Database is developed using Microsoft Visual C programming language.
The developed database was used to assess and control the pollution, as well as the pollution sources were identified. This database consists of four modules which developed to present all study area data in
41
conjunction with the MIKE11 results for different simulation scenarios. 4-6-1 Database Structure Design The database design includes various tables to accommodate the following considerations:A) Integrating the database with the developed water quality MIKE11 Model to enable the decision-maker predicting the water quality concentration along the stream. B) Integrating the WQMIS database to the GIS system provides effective search technique. C) Generating different information reports types, graphs and maps. D) Retrieval of information at different level of security The criteria for selection the database technology are:• Adequate C coding, report generation, as well as dealing with graphs, drawing images and multimedia objects within the same environment. • Process excellent user control and security hierarchy, with comprehensive system auditing and follow-up. • Easy linking with other outside databases, GIS systems and in the Web applications. • Providing robust backup and disaster recovery utilities of different types (Incremental, Dynamic and full Backup). • Behaving efficiency with large size system and intensive data processing environment. 4-6-2 Information System The information system includes three types of information for stream, DWPs, and various water quality parameters. 4-6-3 Developing Graphical User Interface (GUI) Cairo Drinking Water Plants WQMIS includes five modules to deal with study area geographic and hydraulic characteristics, sources of pollution, water quality data, modeling results and reports. WQMIS database was developed through Microsoft Visual C as programming language . The interface of the developed system is constructed using new forms which created with command buttons to perform the needed functions as shown in figure(4-6).
A-Cairo Reach Module This is the first module in GIS application. It consists of three main submodules including the map of Cairo reach along River Nile, study area
42
discharge for various years and Drinking Water Plants database including layout, productivity. These sub-modules displaying the base maps layers. B-Pollution Sources Module This module divided into three sub-modules including drains, waste water from drinking water plants and wastewater from factories. These sub-modules display all pollution sources in the study and their discharges. C-Water Quality Data Module This module includes three sub-modules; the first sub-module displays water quality parameters for gauging station along the study area. The second sub-module displays the surface water quality index. The third sub-modules include forms of water quality parameters displayed in map layers, bar charts and graphs. D-MIKE11 Modeling Module It includes only one sub-module for MIKE11 water quality modeling, which containing all model input and output data. It presents the simulation results for different scenarios. E-Reports Module This module represents the output of WQMIS and includes three sub modules:• Geographic maps (where maps exhibit location, colored entities) • Graphical charts. • Technical reports.
43
Figure (4-6) Cairo Reach Water Quality Management Information System
Chapter (5)
44
Results and Discussion In this chapter assessment of water quality, WQI, and pollution sources determination for the study area are explained. MIKE11 model is explained in details, besides model calibration, and different scenarios, then description of the developed Water Quality Management Information System(WQMIS) and their different modules is explained. 5-1 Water Quality Assessment in order to evaluate the water quality of the study area for public use, irrigation and drinking purposes, three consecutive water quality data sets for years 2012,2013 and 2014 were used and grouped to satisfy model calibration, run and validation requirements respectively. The analyses of water samples were carried for twelve consequence months during these years to show the effect of the spatial and temporal variation. The comprehensive physical-chemical surface water quality analysis was done using APHA (2012) standard method, the generated results were compared with the national standard(law 48/2013 with its ministerial decree 92/2013), and WHO (1989) values for river water quality. Water quality index was calculated based on (CCME WQI 2001) values as stated in methodology described in previous chapter. Table (5-1) illustrates the spatial variation of mean annual pH, DO,TDS, Nitrate, Ammonia, Iron, BOD, COD, FC, and WQI of the study area according to 2013 water quality data set.
45
Table (5-1) Spatial variation of surface water quality parameters and WQI, 2013 Sample No.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Location
pH
After El Saff Town Before Massanda Drain After Massanda Drain Before Ghamaza Soghra Drain After Ghamaza Soghra Drain Before Ghamaza Kobra Drain After Ghamaza Kobra Drain Before Khour Sail El Tibeen After Khour Sail El Tibeen Before Tibeen Power Station After Tibeen Power Station Before Tibeen DWP After Tibeen DWP Before Kafr El Elw DWP After Kafr El Elw DWP Before North Helwan DWP After North Helwan DWP Before Maadi DWP After Maadi DWP Before Fostat DWP After Fostat DWP
7.77±.0.04 7.75±0.04 7.86±0.13 7.88±0.10 7.86±0.14 8.00±0.08 7.98±0.19 8.01±0.29 7.81±0.25 7.88±0.17 7.76±0.18 8.03±0.10 7.98±0.43 8.05±0.20 8.11±0.17 8.12±0.19 7.8±0.24 7.87±0.25 7.88±0.29 8.15±0.29 7.9±0.18
Standard Guidelines (Law48/1982) and WHO (1989)
6.50-8.50
DO (mg/l)
TDS (mg/l)
BOD (mg/l)
COD (mg/l)
F.C. CFU
Iron (mg/l)
Nitrate (mg/l)
Ammonia (mg/l)
WQI Value
WQI Rank
7.41±0.44 7.46±0.31 7.39±0.18 7.43±0.11 7.42±0.16 7.47±0.50 7.43±0.18 7.48±0.09 7.39±0.24 7.37±0.13 7.29±0.14 7.35±0.10 7.26±0.18 7.25±0.15 7.23±0.09 7.19±0.06 7.17±0.21 7.19±0.16 7.15±0.15 7.24±0.09 7.18±0.09 DO ≥ 6.00 (mg/l)
285.66±43.31 288.69±15.10 310.32±12.83 301.99±22.17 312.83±0.71 289.59±29.52 312.84±36.15 290.16±41.30 309.55±33.53 281.17±23.36 302.57±18.29 280.72±15.66 302.42±5.81 285.94±24.11 291.73±16.93 273.68±23.94 300.13±21.81 268.38±16.86 284.25±19.37 268.75±14.99 294.23±23.98
3.48±0.51 3.52±0.12 3.53±0.51 3.49±0.25 3.56±0.30 3.5±0.33 3.55±0.45 3.51±0.19 3.54±0.24 3.53±0.13 3.49±0.15 3.58±0.08 3.59±0.24 3.58±0.40 3.59±0.12 3.56±0.18 3.58±0.08 0.36±0.25 0.39±0.30 0.46±0.11 0.37±0.53 BOD ≤6.00 (mg/l)
17.89±0.40 17.92±0.57 18.11±0.51 17.95±0.16 18.19 ±0.42 17.96±0.71 18.22±0.57 17.95±0.50 18.09±0.48 17.94±0.24 18.09±0.42 17.89±0.28 18.00±0.26 17.86±0.40 17.91±0.30 17.84±0.25 17.90±0.09 0.25±0.09 0.27±0.08 0.22±0.04 0.27±0.03 COD ≤10.00 (mg/l)
1365±110 1375±136 1383±69 1372±127 1389±235 1375±94 1389±162 1372±88 1389±55 1370±116 1385±124 1380±111 1391±111 1387±152 1391±135 1390±83 1395±172 0.3±0.09 0.35±0.04 0.32±0.05 0.36±0.07 FC ≤1000 (MPN/100ml)
0.20±0.03 0.22 ±0.04 0.24±0.04 0.23±0.04 0.31±0.07 0.29±0.03 0.31±0.08 0.28±0.05 0.31±0.05 0.28±0.05 0.31±0.07 0.28±0.04 0.3±0.07 0.31±0.05 0.34±0.06 0.33±0.06 0.30±0.04 3.60±0.30 3.56±0.23 3.58±0.14 3.61±0.16 Iron ≤1.00 (mg/l)
0.41±0.06 0.46±0.33 0.53±0.27 0.5±0.29 0.56 ±0.28 0.43±0.24 0.46 ±0.37 0.39±.23 0.38±0.38 0.33±0.43 0.31±0.13 0.26±0.04 0.30±0.06 0.25±0.05 0.25±0.05 0.23±0.04 0.25±0.07 17.84±0.39 17.93±0.60 17.85±0.24 17.90±0.15 Nitrate ≤ 2.00 (mg/l )
0.22±0.03 0.23±0.03 0.34±0.04 0.22±0.02 0.31±0.02 0.22±0.04 0.29±0.03 0.20±0.04 0.32±0.03 0.21±0.04 0.30±0.05 0.22±0.02 0.25±0.04 0.24±0.03 0.30±0.03 0.23±0.06 0.26±0.07 1399±126 1399±161 1389±91 1398±170 Ammonia ≤0.50 (mg/l)
94.81 95.39 91.39 95.73 94.27 94.17 92.17 94.93 90.90 94.55 93.14 95.20 92.01 94.15 92.93 94.68 92.61 97.36 92.71 93.66 92.35
Good Excellent Good Excellent Good Good Good Good Good Good Good Excellent Good Good Good Good Good Excellent Good Good Good
-
-
TDS ≤ 500.00 (mg/l)
46
Table (5-1) (Continued) Spatial variation of surface water quality parameters and WQI, 2013 Sample No. 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Location Before Gezeret El Dahab DWP After Gezeret El Dahab DWP Before El Roda DWP After El Roda DWP Before Giza DWP After Giza DWP Before Rod El Farag DWP After Rod El Farag DWP Before El Nasser Glass After El Nasser Glass Before Ismailia Canal After Ismailia Canal Before Embaba DWP After Embaba DWP Before Sharkawia Canal After Sharkawia Canal Before Delta Cotton Kanater After Delta Cotton Kanater Before EL Kanater Town
Mean Value Standard Guidelines (Law48/1982) and WHO (1989)
pH
DO (mg/l)
TDS (mg/l)
Nitrate (mg/l)
Ammonia (mg/l)
Iron (mg/l)
BOD (mg/l)
COD (mg/l)
FC (FCU)
7.80±0.28 7.88±0.29 8.12±0.11 7.77±0.18 8.19±0.11 7.79±0.18 8.27±0.08 7.99±0.11 8.05±0.15 7.91±0.18 8.02±0.15 8.03±0.13 8.27±0.08 7.99±0.11 8.01±0.16 8.00±0.13 7.93±0.13 8.01±0.06 7.87±0.09 7.97±0.24
7.13±0.18 7.09±0.15 7.23±0.18 7.17±0.13 7.19±0.18 7.07±0.13 7.26±0.07 7.17±0.09 7.15±0.33 7.14±6.06 7.16±0.27 7.13±0.13 7.26±0.07 7.17±0.09 7.15±0.08 7.15±0.24 7.14±0.09 7.15±0.10 7.13±0.07 7.89±0.31
270.38±17.75 288.25±19.39 277.53±24.47 307.76±28.37 270.50±20.13 309.78±28.37 272.87±25.67 311.27±24.20 286.35±24.53 306.41±47.79 288.05±28.64 261.06±31.53 272.87±25.67 311.27±24.20 279.16±16.33 271.37±16.47 274.69±15.63 278.62±20.55 264.6±14.98 294.04±41.5
0.38±0.21 0.39±0.30 0.28±0.16 0.32±0.18 0.21±0.10 0.35±0.18 0.28±0.09 0.34±0.14 0.25±0.11 0.43±0.05 0.33±0.06 0.29±0.05 0.28±0.09 0.34±0.14 0.3±0.07 0.32±0.05 0.3±0.04 0.24±0.07 0.25±0.03 0.37±0.08
0.22±0.05 0.29±0.08 0.23±0.03 0.27±0.05 0.20±0.07 0.29±0.05 0.25±0.04 0.27±0.02 0.23±0.02 0.35±0.10 0.22±0.05 0.24±0.04 0.25±0.04 0.27±0.02 0.26±0.03 0.24±0.06 0.26±0.03 0.22±0.05 0.21±0.04 0.25±0.05
0.32±0.06 0.38±0.04 0.34±0.09 0.35±0.06 0.31±0.08 0.38±0.06 0.33±0.06 0.34±0.05 0.35±0.07 0.38±0.09 0.38±0.02 0.31±0.05 0.33±0.06 0.34±0.05 0.33±0.06 0.35±0.07 0.34±0.09 0.36±0.06 0.37±0.05 0.32±.10
3.65±0.34 3.59±0.28 3.58±0.33 3.59±0.16 3.49±0.31 3.69±0.16 3.56±0.13 3.6±0.16 3.57±0.28 3.60±0.31 3.56±0.27 3.60±0.21 3.56±0.13 3.6±0.16 3.56±0.26 3.6±0.25 3.57±0.20 3.57±0.21 3.56±0.34 5.33±0.29
17.80±0.34 17.98±0.60 17.83±0.22 18.09±0.44 17.53±0.22 18.12±0.44 17.85±0.13 18.00±0.40 17.87±0.10 17.91±0.34 17.86±0.28 17.91±0.52 17.85±0.13 18.00±0.40 17.84±0.42 17.95±0.45 17.81±0.37 17.94±0.46 18.00±0.20 17.92±1.47
6.50-8.50
DO ≥ 6.00 (mg/l)
TDS ≤ 500.00 (mg/l)
Nitrate ≤2.00 (mg/l )
Ammonia ≤0.50 (mg/l)
Iron ≤1.00 (mg/l)
BOD ≤6.00 (mg/l)
COD ≤10.00 (mg/l)
1392±128 1399±161 1388±86 1399±164 1375±60 1459±164 1384±84 1399±196 1385±128 1399±183 1389±96 1399±151 1384±84 1399±196 1392±132 1399±154 1388±125 1389±208 1395±128 1194±89 FC ≤1000 (MPN/100 ml)
47
WQI
WQI Rank
97.31 92.71 94.70 93.18 94.70 93.29 95.50 92.40 93.53 92.28 92.97 91.52 95.50 92.40 91.77 96.26 90.80 90.17 90.52 9330
Excellent Good Good Good Good Good Excellent Good Good Good Good Good Excellent Good Good Excellent Good Good Good Good
-
-
From table (5-1), it can be summarized:• The mean annual study area pH value is 7.97±0.24. This value is within the permissible limits (6.5-8.5) of the national guidelines(law 48/1982). •
The mean annual study area DO value is 7.89±0.31 mg/l. This value is within the permissible limits (minimum permissible 6mg/l) of the national standard. After different pollution source locations (drains, factories and DWPs wastewater), a relative decrease of dissolved oxygen concentrations can be noted. This may be related to pollutants discharge’s which contain high amount of organic matter.
•
The mean annual study area TDS concentration is 294.04±41.5 mg/l. This value is within the permissible limits (maximum permissible 500 mg/l) of national guidelines(law 48/1982).
•
The mean annual Nitrate concentration for the study is 0.37±0.08 mg/l. This mean value is within the permissible limits (maximum permissible 2.00 mg/l) of the national guidelines(law 48/1982).
•
The mean annual Ammonia concentration for the study area is 0.25±0.05 mg/l. This value is within the permissible limits (maximum permissible 0.50 mg/l) the national guidelines(law 48/1982).
•
The mean annual Iron concentration for the study area is 0.32±.10 mg/l. This value is within the permissible limits (maximum permissible 1mg/l) of the national guidelines (law 48/1982).
•
The mean annual BOD concentration for the study area is 5.33±0.29 mg/l. This value is within the permissible limits (maximum permissible 6mg/l) of the national guidelines (law 48/1982).
•
The study area's COD values showed slight and steady increase from South to North. The mean annual COD concentrations vary from 17.81±0.19 to 18.22±0.23 mg/l. The mean COD value of overall study area is 17.92±1.47 which violate the permissible limits (maximum 10 mg/l) of the national guidelines(law 48/1982). This increase in COD values may be due to the discharge of industrial effluents and other wastes into the Nile by some factories .
48
• The national guidelines has not set a standard value for fecal coliform (FC) counts for the ambient water quality of the Nile River. Therefore, the value given by the WHO (1989) as a guideline for use of water for unrestricted irrigation (FC≤1000MPN/100ml) has been taken as a guide for the evaluation of the water quality in this study. The mean annual FC values for the study area vary from 1370±15 to 1399±22 MPN/100ml. The high mean values of FC may be related to the domestic wastewater discharge into the River Nile. • According to CCME – WQI, study reach water quality can be categorized into two types “Good” and “excellent ". The mean annual WQI values varied from 90.12±1.53 to 97.36±2.09. A relative decreasing of River Nile water quality status expressed by WQI after pollution sources (drains, factories, wastewater from DWPs)locations.
5-2 Study Area Water Quality Modeling In this part water quality model MIKE11 was adopted to simulate the water quality status. This model was calibrated and validated to simulate different scenarios for improving water quality problems in the study area. In this study, three years data sets are used to simulate River Nile at Cairo reach in MIKE11 model. The model was run and analyzed based on water quality data set for year 2013. 5-2-1 Model Calibration Salinity was chosen for calibration process because it is considered a conservative material and it is an excellent water mass tracer. The calibration was done at various points along the study reach by comparing observed EC (μS/cm) with the measured data collected during year 2012. Figures (5-1a) shows the mean annual simulated salinity for year 2012 while Figures (5-1b) and (5-1c) show the comparison between observed and simulated profiles EC (μS/cm) at various study area locations.
49
Figures (5-1a) Mean Annual Simulated Salinity, 2012
Figures (5-1b) GIS Map for Simulated and Observed EC (μS/cm),2012 50
Simulated EC(μS/cm) 2012
364.00
y = 0.5982x + 142.32
362.00
2 R = 0.7613
360.00 358.00 356.00 354.00 352.00 350.00 350
352
354
356
358
360
362
364
366
368
370
Observed EC(μS/cm)2012
Figures (5-1c) Relation between Observed and Simulated Mean Annual EC(μS/cm), 2012 It can be noted from figures (5-1a), (5-1b) and (5-1c) that:• Study reach EC follows a regular trend during the study period at all the calibration points with values varying from 350 to 361 μS/cm. • The observed and modeled EC results indicate a strong linearly correlation between them with a high value of determination coefficient (R2) equal to 0.76, however the simulated results for EC matches very well with the observed values during all the months at different calibration points. 5-2-2 Model Run After calibration of MIKE11 model, the model was successfully executed as described in last sections. The data set used for this model run is water quality data for year 2013. 5-2-2-1 Modeling of Dissolved Oxygen Figures (5-2a), (5-2b) and (5-2c) illustrate the simulated and observed mean annual DO results during the mentioned period.
51
Figure (5-2a) Mean Annual Simulated DO Profile, 2013
Figures (5-2b) GIS Map for Simulated and Observed DO, 2013 52
Simulated DO (mg/l)
7.45 7.40
y = 0.8453x + 1.1063
7.35
2
R = 0.893 7.30 7.25 7.20 7.15 7.10 7.05 7.05
7.1
7.15
7.2
7.25
7.3
7.35
7.4
7.45
Observed DO (mg/l)
Figures (5-2c) Relation between Observed and Simulated DO, 2013 It can be noted from figures (5-2a), (5-2b) and (5-2c) that:• The DO level was higher than 6.0 mgO2/l, indicating the high assimilation capacity of the Nile; however oxygen situation in the study reach is not alarming. • The deviation of simulated DO results from the observed DO within the range from zero to 0.1 mg/l. • The DO level in the river reach decreased to lowest levels due to intervention of the different pollution sources (drains, factories, wastewater from DWPs) in this river segment. • The simulated values of DO were in good agreement with the observed values. 5-2-2-2 Modeling of Biochemical Oxygen Demand Figure (5-3a), (5-3b) and (5-3c) illustrate the simulated and observed mean annual BOD results during the mentioned period.
53
7.5
Figure (5-3a) Mean Annual Simulated BOD Profile,2013
Figure (5-3b) GIS Map for Simulated and Observed BOD, 2013
54
Simulated BOD (mg/l)
3.58
y = 0.3507x + 2.2927 3.56
2
R = 0.5827
3.54 3.52 3.50 3.48 3.46 3.4
3.45
3.5
3.55
3.6
Observed BOD (mg/l)
Figures (5-3c) Simulated and Observed BOD Relation, 2013 It can be noted from the simulated BOD results in figures (5-3a), (5-3b) and (5-3c) that:• The simulated BOD values vary from 3.47 to 3.57 mg/l. • The variation in BOD profile can be justified due to the different types and locations of various pollution sources (i.e. surface drains, waste from factories and drinking water plants sludge disposal). • BOD decreases further downstream in the reach indicating relatively high in-stream purification. • The effect of simulated BOD level is reflected in the simulated DO levels as shown in figure (5-3b). 5-2-2-3 Modeling of Chemical Oxygen Demand Figure (5-4a), (5-4b) and (5-4c) illustrate the simulated and observed mean annual COD results during the mentioned period.
55
3.65
Figure (5-4a) Mean Annual Simulated COD Profile, 2013
Figure (5-4b) GIS Map for Simulated and Observed COD, 2013
56
18.05
Simulated COD (mg/l)
18.00
y = 0.4936x + 9.0064 17.95
R2 = 0.7201
17.90 17.85 17.80 17.75 17.75
17.8
17.85
17.9
17.95
18
18.05
18.1
18.15
18.2
18.25
Observed COD (mg/l)
Figures (5-4c) Relation between Observed and Simulated COD, 2013 It can be noted from the simulated COD results in figures (5-4a), (5-4b) and (5-4c) that:• COD simulated concentrations were not complying with the national standard (law 48/1982) value (10 mg/l). • The simulated values of COD were in good agreement with the observed values. 5-2-2-4 Modeling of Fecal Coliform Figure (5-5a), (5-5b) and (5-5c) illustrate the simulated and observed mean annual FC data during the mentioned period.
Figure (5-5a) Mean Annual Simulated FC Profile, 2013
57
Figure (5-5b) GIS Map for Mean Annual Simulated Fecal Coliform, 2013
Simulated FC (CFU/100mL)
1400.00
y = 0.5312x + 644.09 2 R = 0.7604
1380.00
1360.00
1340.00
1320.00
1300.00 1350
1355
1360
1365
1370
1375
1380
1385
1390
1395
1400
Observed FC(CFU/100mL)
Figures (5-5c) Simulated and Observed FC Relation, 2013 It can be noted from figures (5-5a), (5-5b) and (5-5c) that:•
The simulated Fecal coliform values vary from 1363 to1 388 MPN/100ml.
58
•
•
Simulated Fecal coliform counts in the study reach exceed WHO (1989) standards (1000/MPNml) for use of water for unrestricted irrigation. The simulated FC values matches well with the observed values.
5-2-3 Model Evaluation Statistics Various statistical measures e.g. root mean square error (RMSE), relative means absolute error (MAE)rel, percent bias (PBIAS), Sutcliffe Efficiency and coefficient of determination (R2) were used for error estimation and to assess calibration and validation of the model. The errors between observed and simulated data and the model efficiency were calculated with the help of statistical measures as presented in table (5-2). Table (5-2) Model Evaluation Statistics Parameters DO BOD COD FC
(MAE)rel 0.003 0.008 0.044 0.003
PBIAS 0.168 0.03 0.02 0.20
NSE 0.879 0.59 0.71 0.58
R2 0.89 0.66 0.71 0.76
It can be noted from table (5-2) that:o The relative mean absolute error (MAE)rel value is very small and tend to be equal it's optimal statistics value (Zero) for various simulated results. o The value of PBIAS is positive that showing the model is underestimation bias (modeled value is less than the observed values) for various simulated results. The magnitude of PBIAS is very small suggesting a good accuracy of the model calibrated and validated results. o The model accuracy is further confirmed from high values (close to 1) of NSE and R2 at the calibration points. o It is evident that there is an excellent match of observed and modeled results at different sites of the study area. 5-2-4 Model Validation and Testing The model was validated using 2014 water quality data set. Figure (5-6), Figures (5-7), Figure (5-8) and Figure (5-9) illustrate simulated and observed mean annual DO, BOD, COD and FC respectively represented in GIS maps.
59
Figure (5-6) GIS Map for Mean Annual DO, 2014
Figure (5-7) GIS Map for Mean Annual BOD,2104
60
Figure (5-8) GIS Map for Mean Annual COD, 2014
Figure (5-9) GIS Map for Mean Annual FC,2104
The evaluation statistics were also applied to evaluate the reliability of model validation for DO, BOD, COD, and FC. The errors between observed and simulated data and the model validation were illustrated in table (5-3).
61
Table (5-3) Model Validation Statistics Parameters DO BOD COD FC
(MAE)rel 0.002 0.007 0.002 0.02
PBIAS 0.15 0.44 0.16 0.07
NSE 0.63 0.44 0.75 0.72
R2 0.70 0.84 0.75 0.75
The results in figures (5-6), (5-7), (5-8), (5-9) and table (5-3) for the four mentioned water quality parameters indicate that:o The magnitude of PBIAS is very small suggesting a good accuracy of the model calibrated and validated results. o The model accuracy is further confirmed from high values (close to 1) of NSE and R2 at the calibration points. 5-3 Water Quality Management Scenarios Water quality management scenarios are simulated using 2013 water quality data set. The main objective of this simulation is to propose the alternative solution to improve the water quality upstream Cairo drinking water plants; however six scenarios using MIKE11 HD, AD and EcoLab modules are designated as explained in table (5-4). Table (5-4) Water Quality Management Scenarios Description Scenario Description Base Pre-simulated model with 2013 water quality dataset. Condition Treatment of four polluted drains (El Massanda, Ghamaza Soghra, Scenario (1) Ghamaza Kobra and Khour Sail drains) using wetland technique in order to reduce pollution loads from these drains. Stopping the sludge disposal effluent from the treatment processes of Scenario (2) seven DWPs (Tibeen, Kafr El Elw, North Helwan, Maadi, Fostat , El Roda and Rod El Farag) and applying sludge treatment alternative. Twenty percent increase in study reach discharge over the maximum Scenario (3) discharge in low demand period in order to dilute the effect of pollution concentrations . Scenario (4) Combination of scenario (1), scenario (2) and scenario (3). Treatment of four polluted drains by constructing wastewater treatment Scenario (5) plants to reduce pollution loads from these drains. Scenario (6) Combination of scenario (1), scenario (2) and scenario (5).
62
5-3-1 Scenario (1) Treatment of Four Polluted Drains Using Wetland Technique In this scenario, in-stream wetland system is suggested for treatment study area drains (El Massanda, Ghamaza Soghra, Ghamaza Kobra and Khour Sail) in order to reduce pollution loads from these drains . This technique allows reduction of various pollution concentrations from these drains. Table (5-5) illustrates the expected performance of Wetland System. Table (5-5) The Expected Performance of Wetland System,( Mitsch, 1993) Parameters
Inflow
Outflow
% Removal
TSS (mg/l) BOD (mg/l) COD (mg/l) Total P (mg/l) Total N (mg/l) Total NH4-N (mg/l) FC MPN/100ml
130 40 200 5 12 10 3x105
21 17 92 2.5 5 5 3x104
84 57 54 50 58 50 One Order
Table (5-6) shows the water quality improvement upstream Cairo drinking water plants in scenario(1) compared with the corresponding base condition of 2013 water quality data set. Table (5-6) Water Quality Improvement under Scenario (1) COD (mg/l)
BOD (mg/l)
Scenario (1)
Reduction Percent
15.60
12.65
Base Condition (2013) 1371
17.82
15.64
12.23
19.83
17.81
15.67
2.83
20.06
17.79
3.53
2.84
19.55
El Roda 3.53 Rod El 3.54 Farag Mean Reduction Percent
2.84 2.83
DWP Tibeen Kafr El Elw North Helwan Maadi Fostat
Base Condition (2013) 3.54
Scenario (1)
Reduction Percent
2.82
20.34
Base Condition (2013) 17.84
3.54
2.82
20.34
3.53
2.83
3.54
FC ( MPN/100ml ) Scenario (1)
Reduction Percent
1187
13.42
1380
1188
13.91
12.02
1385
1189
14.15
15.69
11.80
1383
1189
14.02
17.81
15.72
11.73
1382
1189
13.96
19.55
17.79
15.73
11.58
1380
1194
13.48
20.06
17.80
15.73
11.63
1379
1196
13.27
19.96
11.94
63
13.80
It can be noted from table(5-6) that:• Upstream Cairo drinking water plants, an average reduction percentage in BOD level with value of 20.0 %, an average decrease in COD concentrations percentage with value of 11.9% and an average reduction percent in FC level with value 13.8% were achieved under the application scenario(1). • Surface drains (El Massanda, Ghamaza Soghra, Ghamaza Kobra and Khour Sail drains) effluents contribute in varying the studied water quality parameters. However, upstream Cairo drinking water plants, a significant improving in water quality parameters can be achieved by applying wetland technique as an effective treatment method for these drains. 5-3-2 Scenario (2) Treatment of drinking water plant sludge disposal •
In this scenario, stopping the sludge disposal effluent from the treatment processes of seven drinking water plant (Tibeen, Kafr El Elw DWP, North Helwan DWP, Maadi DWP, Fostat DWP, El Roda DWP and Rod El Farag DWP).As illustrated in appendix (1), many alternatives for treatment and disposal CDWPs sludge instead of discharge it into Nile River is proposed . Table (5-7) shows the water quality improvement upstream Cairo drinking water plants due to application scenario(2) compared with the corresponding base condition of 2013 water quality data set. Table (5-7) Water Quality Improvement under Scenario(2) COD (mg/l)
BOD (mg/l) DWP
Base Condition (2013) 3.54
Tibeen Kafr El 3.54 Elw North 3.53 Helwan Maadi 3.54 Fostat 3.53 El Roda 3.53 Rod El 3.54 Farag Mean Reduction Percent
Scenario (2)
Reduction Percent
2.90
18.08
Base Condition (2013) 17.84
3.00
15.25
3.02
FC ( MPN/100ml )
Scenario (2)
Reduction Percent
15.20
14.80
Base Condition (2013) 1371
17.82
15.60
12.46
14.45
17.81
15.80
3.08 3.17 3.20
12.99 10.20 9.35
17.79 17.81 17.79
3.30
6.78
17.80
12.44
Scenario (2)
Reduction Percent
1208
11.89
1380
1209
12.39
11.29
1385
1209
12.71
15.90 16.10 16.20
10.62 9.60 8.94
1383 1382 1380
1209 1211 1211
12.58 12.37 12.25
16.30
8.43
1379
1211
12.18
10.88
12.34
It can be noted from table(5-7) that upstream Cairo drinking plants, an average reduction percentage in BOD level with value of 12.4%, an
64
average decrease in COD concentrations percentage with value of 10.9% and an average reduction percent in FC level with value 12.3% were achieved under the application scenario(2). 5-3-3 Scenario (3) Increasing the Study reach flow up to 20 percent over the maximum discharge in low demand period In this scenario suggestion of increasing the flow reaches by 20 percent over the maximum discharge in low demand period (December, January, February, and March). In practical application, this scenario has a noted limitation in case of water scarcity special under circumstance of expected Nile flow reduction which has emerged recently due to the rapid implementation plans of the Ethiopian Dams. Table (5-8) show the water quality improvement upstream Cairo drinking water plants due to application scenario(3) compared with the corresponding base condition of 2013 water quality data set. Table (5-8) Water Quality Improvement under Scenario(3) COD (mg/l)
BOD (mg/l)
Scenario (3)
Reduction Percent
16.02
10.20
Base Condition (2013) 1371
17.82
16.11
9.60
7.65
17.81
16.20
3.25
8.19
17.79
3.53
3.27
7.37
El Roda 3.53 Rod El 3.54 Farag Mean Reduction Percent
3.24 3.24
DWP Tibeen Kafr El Elw North Helwan Maadi Fostat
Base Condition (2013) 3.54
Scenario (3)
Reduction Percent
3.23
8.76
Base Condition (2013) 17.84
3.54
3.25
8.19
3.53
3.26
3.54
FC ( MPN/100ml ) Scenario (3)
Reduction Percent
1263
7.88
1380
1280
7.25
9.04
1385
1265
8.66
16.15
9.22
1383
1281
7.38
17.81
16.19
9.10
1382
1270
8.10
8.22
17.79
15.99
10.12
1380
1263
8.45
8.47
17.80
16.20
8.99
1379
1261
8.56
8.12
9.47
8.04
It can be noted from table (5-8) that an average reduction percentage in BOD level with value of 8.1%, an average decrease in COD concentrations percentage with value of 9.5 % and an average reduction percent in FC level with value 8.0%were achieved under the application scenario(3). 5-3-4 Scenario (4) Increasing study reach flow, treatment of polluted drains using wetland technique and treatment of drinking water plant sludge disposal In this scenario, a combination between scenarios (1), scenarios (2) and scenario (3) was suggested. Table (5-9) shows the water quality
65
improvement upstream Cairo drinking water plants due to application scenario(3) compared with the corresponding base condition of 2013 water quality data set. Table (5-9) Water Quality Improvement under Scenario(4) COD (mg/l)
BOD (mg/l) DWP Tibeen Kafr El Elw North Helwan Maadi
Base Condition (2013) 3.54
Scenario (4)
Reduction Percent
2.32
34.46
Base Condition (2013) 17.84
3.54
2.35
33.62
3.53
2.33
3.54
Scenario (4)
Reduction Percent
FC ( MPN/100ml ) Base Reduction Scenario Condition Percent (4) (2013) 1371 1116 18.6
15.81
11.38
17.82
15.83
11.17
1380
1117
19.06
33.29
17.81
15.82
11.17
1385
1119
19.21
2.35
33.62
17.79
15.84
10.96
1383
1121
18.94
3.53
2.39
32.29
17.81
15.89
10.78
1382
1124
18.67
El Roda 3.53 Rod El 3.54 Farag Mean Reduction Percent
2.38
32.58
17.79
15.88
11.02
1380
1126
18.41
2.40
32.20
17.80
15.84
11.01
1379
1125
18.42
Fostat
33.25
11.07
18.76
It can be noted from table (5-9) that an average reduction percentage in BOD level with value of 33.3 %, an average decrease in COD concentrations percentage with value of 11.1 % and an average reduction percent in FC level with value 18.8 %were achieved under the application scenario(4). 5-3-5 Scenario (5) Treatment of Four Polluted Drains by constructing wastewater treatment plants In this scenario, construction of new waste water plants is suggested for treatment the effluent of El Massanda, Ghamaza Soghra, Ghamaza Kobra and Khour Sail drains. This technique allows reduction of various pollution concentrations from these drains. Table (5-10) illustrates the expected waste water plants pollutants removal rates.
66
Table (5-10) The expected wastewater treatment plants typical removal rates, (Marcos Von Sperlimg, et al., 2005) Parameter BOD (mg/l) COD (mg/l) SS (mg/l) Total P (mg/l) Metals Coliforms
Primary Secondary Tertiary Treatment Treatment Treatment Cumulative removal percent 30-50 90-95 90-95 30-40 80-90 80-90 40-60 90-95 90-95 5-15 20-40 80-90 30-50 50-70 85-95 30-60 90-99 90-99
A secondary wastewater treatment plants with was chosen for reduction pollution loads from the four indicated drains. Table (5-11) shows the water quality improvement upstream Cairo drinking water plants due to application scenario(5) compared with the corresponding base condition of 2013 water quality data set. Table (5-11) Water Quality Improvement under Scenario(5) COD (mg/l)
BOD (mg/l) DWP
Base Condition (2013) 3.54
Scenario (5)
Reduction Percent
2.19
38.14
Base Condition (2013) 17.84
3.54
2.21
37.57
3.53
2.24
3.54
FC ( MPN/100ml ) Base Scenario Reduction Condition (5) Percent (2013) 1371 1165 15.03
Scenario (5)
Reduction Percent
15.71
11.94
17.82
15.70
11.90
1380
1167
15.43
36.54
17.81
15.73
11.68
1385
1169
15.60
2.23
37.01
17.79
15.69
11.80
1383
1170
15.40
3.53
2.24
36.54
17.81
15.69
1190
1382
1168
15.48
El Roda 3.53 Rod El 3.54 Farag Mean Reduction Percent
2.23
36.36
17.79
15.72
11.64
1380
1168
15.36
2.25
36.44
17.80
15.74
11.57
1379
1171
15.08
Tibeen Kafr El Elw North Helwan Maadi Fostat
37.01
11.78
15.34
It can be noted from table (5-11) that an average reduction percentage in BOD level with value of 37.0%, an average decrease in COD concentrations percentage with value of 11.9% and an average reduction percent in FC level with value 15.3% were achieved under the application scenario(5).
67
5-3-6 Scenario (6) Increasing study reach flow, treatment of polluted drains by constructing wastewater treatment plants and treatment of drinking water plant sludge disposal In this scenario, a combination between scenarios (1), scenarios (2) and scenario (5) was suggested. Table (5-12) shows the water quality improvement upstream Cairo drinking water plants due to application scenario(6) compared with the corresponding base condition of 2013 water quality dataset. Table (5-12) Water Quality Improvement under Scenario(6) COD (mg/l)
BOD (mg/l)
Scenario (6)
Reduction Percent
15.41
13.62
Base Condition (2013) 1371
17.82
15.40
13.58
39.09
17.81
15.43
2.14
39.55
17.79
3.53
2.13
39.66
El Roda 3.53 Rod El 3.54 Farag Mean Reduction Percent
2.11 2.10
DWP Tibeen Kafr El Elw North Helwan Maadi Fostat
Base Condition (2013) 3.54
Scenario (6)
Reduction Percent
2.10
40.68
Base Condition (2013) 17.84
3.54
2.14
39.55
3.53
2.15
3.54
FC MPN/100ml ) Scenario (6)
Reduction Percent
1020
25.60
1380
1019
26.16
13.36
1385
1021
26.28
15.40
13.43
1383
1022
26.10
17.81
15.41
13.48
1382
1017
26.41
40.23
17.79
15.44
13.21
1380
1015
26.45
46.68
17.80
15.45
13.20
1379
1018
26.18
39.92
13.41
26.17
It can be noted from table (5-12) that an average reduction percentage in BOD level with value of 39.7%, an average decrease in COD concentrations percentage with value of 13.4% and an average reduction percent in FC level with value 26.2% were achieved under the application scenario(6). Table (5-13) and figure (5-11) illustrate the improvement in water quality upstream seven drinking water plants (Tibeen, Kafr El Elw, North Helwan, Maadi, Fostat, El Roda and Rod El Farag) DWPs under various water quality management scenarios.
68
Table (5-13) Water quality improvement upstream DWPs under various scenarios Scenario Scenario (1) Scenario (2) Scenario (3) Scenario (4) Scenario (5) Scenario (6)
Mean BOD Reduction Percent 20.0 12.4 8.1 33.3 37.0 39.9
Mean COD Reduction Percent 11.9 10.9 9.5 11.1 11.8 13.4
Mean FC Reduction Percent 13.8 12.3 8.0 18.8 15.3 26.2
45
Reduction Percent
40 35 30 25 20 15
Mean BOD
10
Mean COD 5
Mean FC
0
Senario (1)
Senario (2)
Senario (3)
Senario (4)
Senario (5)
Senario (6)
Scenario
Figure(5-11) Summary of Water Quality Improvement under Different Scenarios
From previous results of management scenarios, it is clear that the behavior of the river upstream Cairo drinking water plants response to varying in water quality conditions. From the absolute view point of water quality improvement only, scenarios(5),(6) and (6) appear the most significant scenarios for water quality improvement. 5-4 Scenarios Evaluation Multi-Criteria Analysis (MCA) that takes four criteria into account; technical, Environmental, Economical, Social and Community criteria were used to find the most sustainable water quality management scenario. Table (5-14) provides a semi-quantitative according to MCA evaluation approach. Figure(5-12) shows MCA total weight score for different water quality management scenarios.
69
Environmental Criteria Economical Criteria
Operation/ Maintenance cost Cost of loss investments
Weighted Score
Score
Weighted Score
Score
Weighted Score
Score
Weighted Score
Score
Weighted Score
0.24
4
0.24
4
0.24
4
0.24
4
0.24
4
0.24
6%
4
0.24
4
0.24
4
0.24
3
0.18
4
0.24
3
0.18
6%
4
0.24
4
0.24
2
0.12
2
0.12
3
0.18
3
0.12
10%
4
0.40
4
0.40
1
0.10
1
0.10
2
0.10
1
0.10
22%
22%
14%
13%
17%
14%
6%
3
0.18
2
0.12
2
0.12
4
0.24
5
0.30
5
0.30
6%
3
0.18
4
0.24
3
0.18
3
0.18
3
0.18
3
0.18
6%
3
0.18
4
0.24
3
0.18
3
0.18
3
0.18
3
0.18
6%
3
0.18
4
0.24
4
0.24
3
0.18
4
0.24
4
0.24
10%
3
0.30
4
0.40
3
0.30
3
0.30
3
0.30
3
0.30
20%
25%
20%
22%
24%
24%
6%
3
0.18
4
0.24
5
0.30
3
0.18
2
0.12
2
0.12
6%
4
0.24
4
0.24
5
0.30
3
0.18
4
0.24
3
0.18
10%
3
0.30
3
0.30
2
0.20
2
0.20
3
0.30
2
0.20
Economical criteria total weight
Social and Community Criteria
Scenario (6)
Scenario (5)
4
Environmental criteria total weight
Initial Cost
Scenario (4)
6%
Technical criteria total weight Surface water quality Protection of ground water Protection of land stability Protection of river habitat Resources use
Scenario (3)
Score
Sustainability
Scenario (2)
Weighted Score
Performance and durability Flexibility and adaptability Resources availability
Scenario (1) Score
Technical Criteria
Primary Criteria and Indicators
Weight
Table (5-14) MCA for Management Scenarios Evaluation
14%
16%
16%
11%
13%
10%
Health and safety risks
10%
3
0.30
3
0.40
4
0.40
3
0.30
4
0.40
3
0.30
Stakeholders acceptability
6%
4
0.24
4
0.24
3
0.18
3
0.18
4
0.24
4
0..24
Social & Community criteria total weight
11%
11%
12%
10%
13%
11%
Management scenario total weight score
68.0%
73.6%
62.0%
55.2%
67.2%
58.8%
70
MCA (Total weight score)
100% 90% 80% 70% 60% 50% 40%
Social & Community
30%
Economical
20%
Environmental
10%
Technical
0% Senario (1) Senario (2) Senario (3) Senario (4) Senario (5) Senario (6) Scenario
Figure(5-12) MCA Total Weight Scores
It can be noted from MCA illustrated in table(5-14) and figure (5-12) that:• •
•
• •
•
MCA total weight score for various management scenario were found 68.0%, 73.6% , 62.0%, 55.2%, 67.2%, and 62.00% for scenario (1), (2), (3), (4), (5) and (6) respectively. Scenario(1) for treatment of study area drains by using wetland technique has a relatively high technical criteria weight but a relatively low social & community criteria weight due to effect of stakeholders acceptability ,Health and safety risks sub criteria evaluation. Scenario(2) for DWPs sludge treatment has the highest overall weight score, total technical and environmental weight scores. However, this scenario can be represent the most convenient scenario for study area water quality management. Scenarios (2) and (3) have the highest economical criteria total weights. Scenarios based on increasing Nile discharge at low flow month such as scenarios(3), (5) and (6) have a relatively low technical criteria total weight due to their sustainability sub criteria inverse effect on compliance with current water management strategy. Scenario(5) for treatment drain discharge by constructing wastewater treatment plants has a relatively high technical weight but a relatively low economical weight.
71
5-5 Cairo Reach Water Quality Management Information System Figure(5-13) shows the main interface for Cairo Reach Water Quality Management Information System.
Figure(5-13) WQMIS Main Interface 5-5-1 Cairo Reach Module This is the first module in the management model. It is divided into three sub modules as shown in figure (5-14). The first sub module gives the user the ability to choose the needed map to display. The second sub module displays Cairo reach discharge for several years. The third sub module displays specific information for Cairo drinking water plants.
Figure(5-14) Cairo Reach Module 72
5-5-2 Pollution Sources Module This module shows different sources of pollution for the Cairo reach. It has three sub modules, which include wastewater from drains, DWPs and factories as shown in figure(5-15).
Figure(5-15) Pollution Sources Module 5-5-3 Water Quality Data Module It includes two sub modules. The first part of this module is the evaluation of water quality trend represented in WQI. The second sub module summarizes some of important water quality parameters for the Cairo reach for several years. These data were presented in different forms as tables, bar-charts, and graphs as shown in figure(5-16).
Figure(5-16) Water Quality Module
73
5-5-4 Modeling Results Module This module includes six sub modules. It includes water quality model calibration, simulation, verification, water quality management scenarios results , scenarios comparison and MCA scenarios evaluation as shown in figure(5-17).
Figure(5-17) Modeling Results Module 5-5-5 Reports Module This module is the output of the database. Reports are obtained either in form of tables, graphics or in the technical reports as shown in figure(518).
74
Figure(5-18) Reports Module
75
Chapter (6) Conclusion and Recommendations In this chapter, conclusions are drawn about the results of the research. Recommendations are proposed and future research issues are also indicated.
6-1 Conclusions Cairo, sits on the River Nile about 160 kilometers south of the Mediterranean Sea, just upstream of the point where the river widens into the Delta. Cairo has an average reach length of 50 km along the river (from Km 900 to km 950 Referenced to Aswan High Dam). The study area covers Cairo governorate along the River Nile, bounded by El Saff town at Km 877.00 from the South and El Kanater town at Km 953.00 from the North. This reach is subjected to different sources of pollution, such as agricultural drainage water , domestic wastewater and industrial wastewater from industrial zones. In this study, the water quality index module was constructed to facilitate estimating water quality index(WQI) in various river locations. It was calculated depending on the Egyptian local standard for year 2013 on monthly basis. Based on water quality index, the water quality in Cairo reach was ranged from good to excellent. However, the WQI study on this reach shows that the water can be used for different purposes. Simulation of the water quality process is done through MIKE11 model after it was calibrated. Based on implemented model simulations, the following conclusions were obtained:• The results of various water quality parameters proved that the water quality at the study area is impacted by a relatively high concentration of COD and FC due to treated or partially treated domestic wastewater and industrial wastewater mixed with agricultural drainage water discharged to the river. • Water quality management scenarios were simulated by MIKE11 water quality model. The main objective of this simulation is to present alternative solution to improve the water quality of the study reach; however, six scenarios using MIKE11 are presented. These scenarios are mainly based on applying various treatment techniques on pollution sources (drains, factories and wastewater from DWPs) for pollution loads reduction and also applying dilution process to reduce the effect of pollution concentrations.
76
Multi criteria decision analysis (MCA) based on four main criteria alternatives through technical, economical environmental and sociocultural criteria, was used to identify the most preferred water quality management scenarios and rank them as a short list of a number of options for subsequent detailed appraisal. From MCA results for evaluation water quality management scenarios, it can be concluded that:• •
•
• •
•
MCA total weight score for various management scenario were found 68.0%, 73.6% , 62.0%, 55.2%, 67.2%, and 62.00% for scenario (1), (2), (3), (4), (5) and (6) respectively. Scenario(1) for treatment of study area drains by using wetland technique has a relatively high technical criteria weight but a relatively low social & community criteria weight due to effect of stakeholders acceptability ,Health and safety risks sub criteria evaluation. Scenario(2) for DWPs sludge treatment has the highest overall weight score, total technical and environmental weight scores. However, this scenario can be represent the most convenient scenario for study area water quality management. Scenarios (2) and (3) have the highest economical criteria total weights. Scenarios based on increasing Nile discharge at low flow month such as scenarios(3), (5) and (6) have a relatively low technical criteria total weight due to their sustainability sub criteria inverse effect on compliance with current water management strategy. Scenario(5) for treatment drain discharge by constructing wastewater treatment plants has a relatively high technical weight but a relatively low economical weight.
Development of an information management strategy and the implementation of suitable database capacity can be important factors to the success of water quality management. Thus, this research also presents Water Quality Management Information System (WQMIS) which include a database sub-system and a graphical user interface. The developed system consists of a number of modules to facilitate data analysis and the presentation of effects of the water quality variables according to the various criteria. The database was designed by using Microsoft Visual C program to be compatible with the available data types and was linked to sub-modules for analysis through various relationships. The main database accounts for Cairo reach, all available water quality data, information related to 77
pollution sources and MIKE11 water quality model. The developed WQMIS links with Geographical Information System (GIS) platform with water quality assessment module. Thus, the developed WQMIS supports the performance of water quality management operations and allows for designing portable application runs under multiple platforms and open systems. The sub modules are linked through a simple Graphical User Interface (GUI). The developed interface is functional and flexible to facilitate water quality control. Reports of various types are made to support water quality management requirements. They can be graphical charts, geo-referenced maps or tabular reports. The developed system is able to support multi-users for multitasks using a variety of data types simultaneously. Therefore this study might assist the decision makers in the pollution management and improve water quality upstream Cairo drinking water plants. Moreover, the study WQMIS can introduce a great value for water users (public), planners, policy makers, and scientists reporting on the state of the environment.
6-2 Recommendations It is recommended to solve the various drains pollution problem and its tributaries to improve Cairo reach water quality through the suggestions in this study as discussed before. It is high preferred to modify the national guidelines (Law 48/1982) to state many effective restriction on different agricultural, industrial and domestic discharge disposal into the Nile River to ensure a sustainable water quality control. The required modification must set different allowable pollution discharge into the river as a hydraulic loads base instead of concentration base. Water quality monitoring of the river provides basic data to be used for detailed analysis and modeling. The sampling of water and wastewater involves intensive traveling and financial resources. Water quality of the rivers and surface drains should be regularly monitored with proper planning and as a joint effort of government agencies and research institutions. In many cases, it is more feasible to establish permanent monitoring stations at highly polluted sites by involving local community in the adjacent vicinities of the river. This can also be helpful in creating a public awareness about the water quality issue.
78
Treatment and reuse of drinking water plants sludge discharge is very essential to reduce Nile river pollution instead of get rid of which sludge discharges into Nile River. The data revealed that pollution assessment of the Nile river could be carried out easily by applying the assessment module. This module links information of chemical characteristics of the discharged effluent from industrial and domestic sources to characteristics of the streams subject to pollution. Environmental education is necessary to provide the people with the necessary knowledge, values, and commitment to participate both individually and collectively to help solve the environmental problems of the study reach.
79
References Abdel-Satar,A.M.,M.E.Goher and M.F. Sayed , 2010 Recent Environmental changes in water and sediment quality of Lake Qarun. Egypt. Journal of Fisheries and Aquatic Science, 5(2):5669. Abdelbary, R., Attia, K., and Galay, V., “River Nile Bank Erosion Development Below the High Aswan Dam”, National Seminar on Physical Responses of the River Nile to Interventions, Edited by Anstey, T. H., and Shady, A. M., Cairo, Egypt, 12-13 November, 1990. Abd El-Daiem, S., 1994. Water quality issues in Egypt. In the roceeding of Italian–Egyptian Study Days on the Environment Conference, Cairo, Egypt, pp: 9-81. Abd El-Daiem, S., 2011. Water Quality Management in Egypt. Journal of Water Resources Development, 27(1): 181-202. Abd El-Hamid, M.I., S.A. Shaaban-Dessouki and O.M Skulberg, 1992. Water Quality of the River Nile in Egypt. 1-physical and chemical characteristics, Journal of Arch. Hydrobiol., Suppl., 90(3): 283-310. Abdo, M.H. (2010):Environmental and water quality evaluation of Damietta branch, River Nile, Egypt. African J. Biol. Sci., 6: 143-158. 8. Abdo, M.H.; Sabae, S.Z.; Haroon, B.M.; Refaat, B.M. and Mohammed, A.S. (2010): Physico-chemical characteristics, microbial assessment and antibiotic susceptibility of pathogenic bacteria of Ismailia Canal water, River Nile, Egypt. J. of Amer. Sci., 6(5): 234-250. Abdo, M.S.E., Ewida, K.T. and Youssef, Y.M. (1993. Recovery of alum from waste sludge produced from water treatment plants. Abdo M.H., S.Z. Sabae, B.M. Haroon, B.M. Refaat and A.S. Mohammed, 2010. Physico-Chemical Characteristics, Microbial Assessment and Antibiotic Susceptibility of Pathogenic Bacteria of Ismailia Canal Water, River Nile, Egypt. Journal of American Science, 6(5): 234-250.
80
Abou El-Ftouh., (2013)," Surface Water Quality Management Case Study: Rosetta Branch-River Nile", Ain Shams University, Cairo, Egypt. ADRIANO A., BORDALO RITA, TEIXEIRA W., WIEBE J. (2006), A Water Quality Index Applied to an International Shared River Basin: The Case of the Douro River, Environ Manage (2006) 38:910–920, DOI 10.1007/s00267-004-0037-6 Agricultural Policy Reform Program, 2002. Survey of Nile System Pollution Sources, Ministry of Water Resources and Irrigation, Agricultural Policy Reform Program Water Policy Program, Report No. 64. Ahmed, N.A.M. (2012): Biochemical studies on pollution of the River Nile at different stations of Delta barrage (Egypt). Ph.D. Thesis, faculty of Agricultre, Benha Univ., Egypt. American Public Health Association, 2005. Standard Methods for the Examination of Water and Wastewater. APHA, AWWA, WPCE, N. Y. Washington. Ambrose, R.B. Jr., Wool, T.A., Connolly J.P. and Schanz R.W. (1988). WASP4, A hydrodynamic and water quality model-Model theory, user's manual and programmer's guide, Report EPA/600/3-87/039, U.S. EPA, Athens, GA, USA. A.P.H.A (1998). Standard Methods for the Examination of Water and Wastwater. 20th.Ed. America Public Health Association. 1015 Fifteenth Street.NW. Washington, DC. 20005-2605. Aragones-Beltran, P., J.A. Mendoza-Roca and A.Bes-Pia, M.GarciaMelon, E.Parra-Ruiz (2009) “Application of multicriteria decision analysis to jar-test results for chemicals selection in the physicalchemical treatment of textile wastewater” Journal of Hazardous Material, 164, pp. 288-295. ARONER E. (2002), WQHydro: Water Quality-Hydrology Statistics/Graphics/Analysis Package. WQHydro Consulting, Portland, OR; CUDE C. (2001), Oregon Water Quality Index: a tool for evaluating water quality management effectiveness, J. Am. Water Resour. Assoc. 37 (1) 125-137. Arup, K.S and Bo, S. (1992). Selective alum recovery from clarifier sludge. J. Am. Wat. Works Assoc. 84(1), 96-103 AWC., 2009. Middle East and North Africa Region/Arab Countries Regional Report. 5th World Water Forum, Istanbul. The Arab Water Council (AWC), Egypt.
81
AWWA committee report, (1981). Lime softening sludge treatment and disposal. J. Am. Wat.Works Assoc. 73(11), 600-608 Babatunde Akintunde, Yang Yongzhe and Zhao Yaqian.(2005). Belton V. & Stewart T. (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic, Dodrecht. Bhatti. M (2009)," srtrategic analysis of spatial and temporal water quality of river chenab and its management" University of Engineering and Technology, Lahore, Pakistan. Burgman, M. (2005), Risks and decisions for conservation and environmental management. After; Suter, G. W. (1993). Ecological Risk Assessment. Boca Raton, Florida: Lewis. 1995. Cairo water company, Central laboratory annual technical report, 2014. Carvalho, M. and Antas, A. (2005). Drinking water sludge as a resource. In: Proceedings of IWA specialised conference on management of residues emanating from water and wastewater treatment, Johannesburg, South Africa. CCME. (2001). "Canadian water quality guidelines for the protection of aquatic life: Canadian Water Quality Index 1.0 Technical Report". Canadian Environmental Quality Guidelines, Winnipeg, Manitoba CCME. (2003). “Canadian water quality guidelines for the protection of aquatic life: chapter 4”. Canadian Environmental Quality Guidelines, Winnipeg, Manitoba. Christopher, A.J.and Jeffory, A.H. (2003. Using treatment residuals to reduce phosphorus in agricultural runoff. J. Am. Wat. Works Assoc. 95(4), 151158. Chu, W. (2001). Dye removal from textile dye wastewater using recycled alum sludge. Wat. Res. 35(13), 3147-3152. CLEQM, (2004, 2006, 2008), Central Laboratories for Environmental Quality monitoring: Analytical results of Rosetta branch water quality.(Unpublished report), El-Kanater Egypt. Codling, E.E., Chaney, R.L and Mulchi, C.L. (2000). Use of aluminium and iron-rich residues to immobilize phosphorus in poultry litter and litter amended soils. J.Environ. Qual. 29(6), 1924-1931 Cox, A.E., Camberato, J.J. and Smith, B.R. (1997). Phosphate availability and inorganic transformation in an alum sludge-affected soil. J.Environ. Qual.26(5), 1393-1398. 82
Daboor, S. M. (2006): Studied on bacterial flora and its role in the clean up of hazardous pollutants in the River Nile, Ph.D. Thesis, Fac. Sci. Zagazig Univ., Egypt Dayton, E.A., and Basta N.T. (2001). Characterization of drinking water treatment residuals for use as a soil substitute.Water Environ. Dhage, S.S, Paramasivam, R., Ravindar, R. and Andey, S.P. (1985). Recovery of alum from water treatment sludge by liquid ion exchange (LIE) technique. J. Indian Wat. Works Assoc. 17(2), 193-199 DeWolfe, J.R. (1988). Land disposal of drinking water sludge. In: Proceedings of American society of Agricultural Engineers, International winter meeting, Illinois.9pp. Dhage, S.S, Paramasivam, R., Ravindar, R. and Andey, S.P. (1985). Recovery of alum from water treatment sludge by liquid ion exchange (LIE) technique. J. Indian Wat. Works Assoc. 17(2), 193-199 Dharmappa, H.B., Hasia, A. and Hagare, P. (1997). Water treatment plant residuals management. Wat. Sci. & Tech.35(8), 45-56 Donald, P.P. (1968). Selection and disposal methods for water treatment plant wastes. J. Am.Wat. Works Assoc. 60(6), 674-680. Doley, T.M., Benelmoouffok, D., and Deschaine, L.M. (2001). Decision support for optimal watershed load allocation. Paper presented at the Society for Modeling and Simulation International Conference, Seattle, 22-26 April. Dymaczewski, Z., Edwards, S.K. and Marek, M.S. (1997). Coagulation as a structure- forming separation process in water and wastewater treatment. Wat. Sci. & Tech. 36(4), 25-32. Ebeid, N. and A. Hamza, 1999. Prevention of industrial pollution to the Nile: A success story. In the proceedings of the seventh Nile 2002 Conference, Comprehensive Water Resources Development of the Nile Basin: the Vision for the Next Century, Cairo, Egypt, pp: 6.1-6.12. Egypt Second National Communication, 2010. Under the United Nations Framework Convention on Climate Change (UNFCCC). Egyptian Governmental Law No. 48, 1982. The Implementer Regulations for law 48/1982 regarding the protection of the River Nile and water ways from pollution. Map. Periodical Bull., 3-4: 12-35
83
Elliot, H.A. and Singer, L.M. (1988). Effect of water treatment sludge on growth and elemental composition of tomato shoots. Commun. Soil Elliot, H.A., Dempsey, B.A. and Maille, P.J. (1990). Content and fractionation of heavy metals in water treatment sludges. J. Environ. Qual. 19(2),330-334. Elliot, H.A., O’Connor, G.A., Lu, P. and Brinton, S. (2002). Influence of water treatment residuals on phosphorus solubility and leaching. J. Environ Qual. 31(4), 1362-1369 Elizabeth, A.D., Nicholas, T.B., Ellis, J.B. 2000. Infiltration systems: A sustainable source control option for urban stormwater quality management?. Journ. Chart. Inst. Water & Envir. Managt., 14, 27- 34. El-Kady, M., 1997. Egypt's Water Resources Development and Strategy for Sustainable Environment. In the proceeding of International Conference on Water Management, Salinity and Pollution Control Towards Sustainable Irrigation in the Mediterranean Region, Bari, Italy, vol. II, pp: 1-22. El-Motassem, M., A. El-Sherbini and M. Ezo, 1996. Environmental Issues Related to Nile Water Quality. In the Proceeding of Nile 2002 Conference, Kambala, Uganda, pp: 1-12. El-Sayed, S. (2011): Physicochemical studies on the impact of pollution up on the River Nile branches, Egypt. M.Sc. Thesis Faculty of Science, Benha University, Egypt. El-Sherbini, A., M. El-Moattassem and H. Sloterdijk, 1992. Water Quality Condition of the River Nile. In the proceeding of Conference of Protection and Development of the Nile and other Major Rivers, Cairo, Egypt, 2/2: (7-1-1)-(7-1-15). European Union. (2010,a). EU EQSD CIS Technical Guidance Document – Mixing Zones. Common Implementation strategy guidance on setting mixing zones under the EQS Directive (2008/105/EC) Farag.A. H (2004) A Methodology applied to water quality management for surface water, Ph.D. Thesis, Ain Shams University, Egypt. FISCHER,II.B.,E.J. LIST. R.C.KOII,J, IMBEHGER,AND H.BROOKS. 1979. Mixing in inland and coastal waters, Academic.
N.
Galarneau, E., and Gehr, R. (1997). Phosphorus removal from wastewaters: Experimental and theoretical support for alternative mechanisms. Wat.Res. 31(2),328-338. Gallimore, L.E, Basta, N.T, Storm, D.E, Payton, M.E., Huhnke, R.H.
84
and Smolen M.D.(1999). Water treatment residual to reduce nutrients in surface runoff from agricultural land. J. Environ Qual. 28(5), 14741478. Garfi, M., Ferrer-Marti, L., Bonoli, A. & Tondelli, S., 2011. Multicriteria analysis for improving strategic environmental assessment of water programmes. A case study in semi-arid region of Brazil. Journal of Environmental Management, Volume 92, pp. 665-675. Goodwin P. & Wright G (2009) Decision Analysis - for Management Judgment. In:1Wiley, A John Wiley and Sons, Ltd., Publication, The Atrium, Southern Gate,Chichester, West Sussex, PO19 8SQ, United Kingdom, pp 454. HALLOCK O. (2002), A water quality index for ecology’s stream monitoring program, Washington State Department of Ecology, Olympia, WA. Publication No 0203052; HOUSE M. A., ELLIS J. B. (1981), Water quality indices: an additional management tool, Water Science and Technology 13:413–423. Heaney, J P., Wright, L., Sample, D., Urbonas, B., Mack, B W., Schmidt, M F., Solberg, M., Jones, J., Clary, J. and Brown, T. 1997. Development of methodologies for the design of integrated wet weather flow collection/control/treatment systems for newly urbanised areas. Vol.1, draft technical report, US Environmental Protection Agency, Edison, New Jersey, United States. Horth, H., Gendebien, A., Agg, R. and Cartwright, N. (1994). Treatment and disposal of waterworks sludge in selected European countries. In: Foundation for water research technical reports No.FR 0428. Hossain M D and Bache D H. (1991). Composition of alum flocs derived from a colored, low turbidity water. J. Water SRT - Aqua 40(5), 298 - 303. HOUSE M. A., ELLIS J. B. (1987), The development of water quality indices for operational management, Water Science and Technology 19:145–154. HOUSE M. A. (1989), Water quality indices as indicators of ecosystem change, Environmental Monitoring and Assessment , Volume 15, Number 3, 255-263, DOI:10.1007/BF00394892. Howard AF (1991) A critical look at multiple criteria decision making techniques with reference to forestry applications. Can J For Res 21:1649–1659. Huang, C., Pan, J.R., Sun, K.D. and Liaw, C.T. (2001). Reuse of water 85
treatment plant sludge and dam sediment in brick making. Wat. Sci. & Tech. 44(10), 273-277. Huang, S.H and Chiswell, B. (2000). Phosphate removal from wastewater using spent Alum sludge. Wat. Sci. & Tech. 2(3-4), 295300. HÜLYA BOYACIOGLU (2007), Development of a water quality index based on a European classification scheme, AJOL,Water SA, Vol. 33 No. 1. Husssien, T., 1998. Water Quality Monitoring for Some Egyptian Canals. In the proceeding of Conference on Coping With Water Scarcity, Hurghada, Egypt, pp: (4-2-1)-(4-2-7). Hyde, J.E. and Morris, T.F. (2004). Phosphorus availability from phosphorus-ammended water treatment residual. Commun.Soil Sci.Plant Anal. 35(7-8), 1033-1046 IWA. 2000. Constructed Wetlands for Pollution Control: Processes, Performance, Design and Operation. London: IWA Publishing. Janssen, R. and Munda, G. (1999). Multi-criteria Methods for Quantitative, Qualitative and Fuzzy Evaluation Problems. In J. C. J. M. van den Bergh (Ed.), Handbook of Environmental and Resource Economics. Cheltenham: Edward Elgar, 837-852. Joo-Hwa, T. and Kuan-Yeow, S. (1991). Properties of Cement made from sludge. J. Environ. Eng. 117(2), 236-246. Kikuchi, R. (2001). Recycling of municipal solid waste for cement production: Pilot-scale test for transforming incineration ash of solid waste into cement clincker. J. Resources, Conservation and Recycling 31(2), 137-147 Kim, J.G. Kamal, M.M., Malmgren-Hansen, A., and Badruzzaman, A.B.M. (1999). Assessment of pollution of the river Buriganga, Bangladesh, using a water quality model. Water Science and Technology, 40 (2): 129–136. Kees, B., 1996. The Use of Decision Support Systems in Water Management. In the Proceeding of CEDARE Regional Seminar, Damascus, Syria. Khan, A.T., and Vongvisessomjai, W. (2002). Mike 11 Application for Salinity Intrusion in Southwest Bangladesh. Proceedings of 1st AsiaPacific DHI Software Conference, 17-18 June. 2002. Bangkok, Thailand.
86
Kim, J.H., Moon H., Chon C., and Ahn, J.S. (2003). Removal capacity of water plant alum sludge for phosphorus in aqueous solution. Chemical Speciation and Bioavailability 14, 67-73. Kim, A.G. and Cardone, C.R., (2005), Scatterscore: a reconnaissance method to evaluate changes in water quality, Environmental Monitoring and Assessment, 111, pp 277-295.IS, Indian Standard Drinking Water Specifications IS: 10500. Bureau of Indian Standards, New Delhi, 1991. LAG-I-00-99-00017-00 Task Order 815 "Water Trading in Fayoum and Frafra Oasis" report No. 72. Lai, J.Y. and Liu, J.C. (2004). Co-conditioning and dewatering of alum sludge and waste activated sludge. Wat. Sci. & Tech. 50(9), 41-48 Leader, J. W. Lai E., Lundie S. & Ashbolt N. J. (2008). Review of multi-criteria decision aid for integrated sustainability assessment of urban water systems. Urban Water Journal.5(4). Li, P. and Sengupta, A.K. (1995). Selective recovery of alum from clarifier sludge using composite ion exchange membranes. In: Proc.of 27th mid Atlantic Ind.Waste Conf., Bethlehem, PA (Technomic). Lin, Y.F., Jing, S.R., and Lee, D.Y. (2001). Recycling of wood chips and wheat dregs for sludge processing. Bioresource Tech.76(2), 161163. Lindsay, R.J. and Logan, T.J. (1998). Manufactured topsoil from EQ biosolids and other byproducts. In: Proc.12thAnnual Residuals Biosolids Management conference Linkov, I., F.K. Satterstrom, G. Kiker, C. Batchelor, T. Bridges & E. Ferguson, 2006a. From comparative risk assessment to multi-criteria decision analysis and adaptive management: Recent developments and applications. Environ. In. 32(8), 1072–1093. liou sm., Liens s. and wang sh. (2004), Generalized water quality index for Taiwan, Environ. Monit. Assess. 96 35-52. LUMB, ASHOK, HALLIWELL, DOUG, TRIBENI, SHARMA (2006), Application of CCMEwater Quality Index to monitor water quality: a case of the Mackenzie river basin, Canada, Environmental Monitoring and Assessment (2006) 113: 411–429, doi: 10.1007/s10661-005-9092-6. Mabrouk, D.R. (2004): Aspects of Manzalah Lake pollution on Mugil species, M. Sc. Thesis, Fac. of Girls, Ain Shams, Univ., Egypt p. 293. Marcos von Sperling and Carlos Augusto de Lemos Chernicharo, 87
Bilogical wastewater treatment in warm climate region,(2005), Brazil. Mark, M.B., Rolan, A.T., Tom, L.B. and David, A.C. (1987). Testing of alum recovery for solids reduction and reuse. J. Am. Wat. Works Assoc. Moore, P.A and Miller D.M. (1994). Decreased phosphorus solubility in poultry litter with aluminium, calcium and iron amendments. J. Environ. Qual. 23(2),325-330. Moran, R.J. and Charles, P.R. (1960). Recovery and reuse of alum sludge at Tampa. J. Am Wat. Works Assoc.52 (7), 857-866. Normativului privind clasificarea calităţii apelor de suprafaţă în vederea stabilirii stării ecologice a corpurilor de apă (ORDIN nr. 161/16 februarie 2006). Morsy, U.A., M.M. Hamza, E.A. Rae and N.F. Nashed, 2002. Geotechnical Aspects, Options Evaluation and Safety Analyses for Assiut Barrage, Egypt. In the proceeding of Annual Conference of the Canadian Society for Civil Engineering. Moustafa, M.M.; Ali, M.H.H.; Abdel-Satar, A.M.; Mohamed, T.Y. and Madbouly, S.M. (2010): Water quality assessment of Rosetta and Damietta branchs, River Nile, Egypt. African J. Biol. Sci., 6(2): 127142. MSEA., 2001. The National Environmental Action Plan of Egypt 2002/17: Environment at the Center of Modernizing Egypt. Unpublished discussion paper, Ministry of State for Environmental Affairs (MSEA), Egypt MWRI., 2005. National Water Resources Plan 2017: Water for the Future, Ministry of Water Resources and Irrigation (MWRI), Egypt. MWRI/ USAID., 2003a. Egypt Water Policy Reform Contract No. LAG-I-00-99-00017-00 Task Order 815 "Nile River Water Quality Management Study" report No. 67. MWRI/ USAID., 2003b. Egypt Water Policy Reform . MWRI, (2004); “The Egyptian Water Strategy Till 2017’, Planning Sector - Ministry of Water Resources and Irrigation, Cairo, Egypt. NWRP. National Water Resource Plan 2017.National Water Resource Plan, Cairo: Arab Republic of Egypt: Ministry of Water Resources and Irrigations, 2005
88
MWRI, (2010); “Water Resources Development and Management Strategy in Egypt – 2050”, in Arabic, Ministry of Water Resources and Irrigation, Cairo, Egypt. Novak, J.M.and Watts, D.W. (2004).Increasing the phosphorus sorption capacity of southeastern coastal plain soils using water treatment residuals. Soil Sci.169 (3), 206-214 Owen, P.G. (2002). NWRC/PFRA., 2008. National Water Quality and Availability Management: National Water Quality Monitoring Component 100. Final Report, National Water Research Center (NWRC) and Agriculture and Agri-Food Canada (PFRA), Egypt. Pecku, S. Hunter, C.H. and Hughes, J.C. (2005). The effects of water treatment residues on soil respiration and microbial community structure. In: Proceedings of IWA specialised conference on management of residues emanating from water and wastewater treatment, Johannesburg, South Africa. Peters, J.M and Basta, N.T. (1996). Reduction of excessive bioavailable phosphorus in soils by using municipal and industrial wastes. J. Environ. Qual. 25(6), 1236-1241. Petruzelli, D., Volpe, A., Limoni, N. and Passino, R. (2000). Coagulants removal and recovery from water clarifier sludge . Wat. Res.34(7), 2177-2182. Petruzelli, D., Limoni, N., Tiravanti, G and Passino, R. (1998. Aluminium recovery from water clarifier sludges by ion exchange. Comparison of weak and strong electrolyte cation resin performances. Prakash, P., Hoskins, D., and Sengupta, A.K. (2004). Application of homogeneous and heterogeneous cation-exchange membranes in coagulant recovery from water treatment plant residuals using Donnan membrane process. J. Membrane Science, 237(1-2), 131-144. Prakhar, P.E. and Arup, K.S. (1998), Donnan Membrane Process: Principles & Application in Coagulant Recovery from Water Treatment Plant Residuals , Lehigh University, Bethlehem, PA. Przedwojski, B., Blazejewski, R. and Pilarczyk, K. W. 1995. River Training Techniques: Fundamentals, Design and Applications, Balkema, Netherlands, 625pp. Radwan, M. and El-Sadek, A. (2005). Control Structures Impact on River Protection and Development. Proceedings of ICID 21st European Regional Conference, 15-19 May 2005 - Frankfurt (Oder) and Slubice Germany and Poland.
89
Rasslan, M. and Abd El Bary, A. (2001). Economical and Environmental aspects of Navigation Development in the Nile. Sixth International Water Technology Conference (IWTC). Rauch, W., Henze, M., Koncsos, L., Reichert, P., Shanahan, P., Somlyódy, L. and Vanrolleghem. P. (1998). River water quality modeling: State of the art. Proceedings of the IAWQ Biennial International Conference. 21-26 June 1998. Vancouver, British Columbia, Canada. Res. 73(1), 52-57 de-Bashan, L. E. and Bashan, Y. (2004. Recent advances in removing phosphorus from wastewater and its future use as fertilizer (1997-2003). Wat.Res.38(19), 4222-4246. Rosén, L., Back, P-E., Soutukorva, Å., Söderqvist, T., Brodd, P., Grahn, L. (2009). Multikriterieanalys (MKA) för hållbar efterbehandling av förorenade områden, metodutveckling och exempel på tillämpning. Naturvårdsverket Report no: 5891. Februari 2009. Rossi, G., Cancelliere, A. & Giuliano, G., 2005. Case study: Multicriteria assessment of drought mitigation measures. Journal of Water Resources Planning and Management, 131(6), pp. 449-457. Rita, N., Kumar, Rajal Solanki and Nirmal Kumar, J.I. “An assessment of seasonal variation and water qualit index of Sabarmati River and Kharicut canal at Ahmedabad, Gujarat”, Electronic Journal of Environmenal, Agricultural and Food Chemistry, Vol. 10, No. 5,pp. 2248-2261, 2011. Roy, B. and Vincke, P. (1981). Multicriteria Analysis: Survey and New Directions. European Journal of Operational Research 8, 207-218. Roy, B. (1993). Decision Science or Decision-aid Science? European Journal of Operational Research 66, 184-203. Sabae, S.Z. (2004): Monitoring of microbial pollution in the River Nile and the impact of some Human activities on its waters, Proc. 3 rd Int. Conf. Biol. Sci. Fac. Sci. Tanta Univ., 3: 200-214. Salgado, P. et al., 2009. Participative multi-criteria analysis for the evaluation of water governance alternatives. A case in the Costa del Sol (Malaga). Ecological Economics, Volume 68, pp. 990-1005. Samuels, P. G. 1990. 'Cross-section location in 1-D models', in White, W. R. and Bettess, R. (Eds), International Conference on River Flood Hydraulics, John Wiley & Sons, Chichester, 439-450. Saravanakumar, K. and R. Ranjith, Kumar, (2011), Analysis of water quality parameters of groundwater near Ambattur industrial area, Tamil 90
Nadu, India, Indian Journal of Science and Technology, 4(5), pp 17321736. Stachowicz, K., 1983. Quality of Water in the River Skawinka Catchment Basin (Souther Poland) in Periods of Low Water Level", Journal of Acta Hydrobiology, 30(1/2): 141-155. TRUFAŞ V. (1975), Hidrochimie, Tipografia Universităţii din ucureşti, Bucureşti; VARDUCA A. (1997), Hidrochimie şi poluarea chimică a apelor, Colecţia Tempus, Ed. H.G.A., Bucureşti. Qureshi, M. E., Harrison, S. R. & Wegener, M., 1999. Validation of multicriteria analysis models. Agricultural Systems, Volume 62, pp. 105-116. United States Environmental Protection Agency, U.S. EPA (1996).The Metals Translator: Guidance for calculating a total recoverable permit limit from a dissolved criterion. EPA 823-B-96-007, Washington. DC. Warwick, J. J. and Heim, K. J. 1995. 'Hydrodynamic modelling of the Carson River and Lahontan Reservoir, Nevada', Water Resources Bulletin, 31, 67-77 Watwer Quality Index Calculator, by: Mr. Brian Oram, PG, According to the book Field Manual for Water Quality Monitoring, http://www.waterresearch.net/watrqualindex/index.htm,(2010), USA. WHO (1989) Health Guidelines for the Use of Wastewater in Agriculture and Aquaculture.Report of a WHO Scientific Group, Technical Report Series No. 778, WHO, Geneva. Wladis, D., Rosén, L., and Kros, H. (1999). Risk-based decision analysis of atmospheric emission alternatives to reduce ground water degradation on the European scale. Ground Water 37(6):818-826. Yehia, H. and S. Sabae, 2011. Microbial Pollution of Water in El-Salam Canal, Egypt. American-Eurasian Journal of Agricultural & Environmental Science, 11(2): 305-309.
91
List of Appendixes Appendix (1): Drinking Water Plants Wastes Treatment and Disposal Appendix (2): In-stream Wetland System Appendix (3): MCA Benchmark Indicators Appendix (4): Sample of Microsoft Visual C Codes
92
Appendix (1): Drinking Water Plants Wastes Treatment and Disposal 1-Sources of Drinking Water Plants Wastes Both solid and liquid wastes are generated by a number of operations used to prepare potable water for public consumption and to remove impurities prior to industrial use. The two primary sources of these wastes are: (A) Sludges: are the residual results of various treatment processes in drinking water plants and include undesirable materials from raw water. Typically, these materials include sand, silt, organics (in solution and suspension), calcium and magnesium ions, and other materials which effect water quality (B) Wash water: are the results of backwashing of sand filters. These wastes are continuously produced and their composition is variable, depending upon the generating mechanism, the quality of water being treated, and the type and amounts of supplemental chemicals used. Figure (1) shows production and disposal of drinking water plants sludges.
93
Figure(1)Production and disposal of drinking Sludges in a Drinking Water Plants Source: Moodley M, Hughes JC, 2005.
2-Nature of drinking water plants sludge The nature of drinking water plants sludge depends on the type of coagulants and other treatment chemical used for the water treatment. Sludge is in particulate or gelatinous mainly consisting from microorganisms, organic and suspended matter, coagulants and other chemical elements. The water treatment sludge can be divided as follows:• Rough trash caught in trash-racks. The character is really various. This sludge contains little water only. The quantity of this trash is very low. • Floc suspensions of iron and alumina oxides from thickening sections in settlement tanks and clarifiers. This sludge contains much water. • Filter washing sludge (the washing wastewater). This sludge is flocculent with small flocs. • Decarbonization sludge. Sediments are more compact than flocculent sludge. The decarbonisation sludge is sometimes used as a fertilizer in the agriculture and does not represent a major issue. • Polymeric flocculant clarification sludge. This sludge contains typically suspended and colloidal particles contained in the treated water. Since fed quantities are rather low, the contents of the polymeric flocculants is low too. • Other water treatment sludge. This is the sludge from slow sand filters, sluicing filter sludge and activated carbon washing sludge. • Wastes produced in preparation of chemicals for the wastewater treatment. 3-Methods Employed to Treat and Dispose Drinking Water Plants Wastes A number of different approaches are used by DWPs operators to handle and dispose of sludges and backwash water. These approaches are primarily based upon the principle of concentrating the solids in the wastes to reduce their volume and facilitate solids handling and disposal. The selection of a specific process alternative depends on such factors as 94
land availability, distance to landfill, sludge character, and maintenance requirements. The various sludge disposal options are shown Figure(2):
Figure(2) Sludge Disposal Options Source: Rakh MS, Bhole AB, 2011.
The most common sludge disposal alternatives can be summarized as follows:3-1 Discharge sludge to a Stream or River
95
In this case, the discharged effluent should satisfy the regulations and laws guidelines to control the levels of dissolved and suspended contaminants in the rivers discharge. A water quality management is required including construction and operation of wastewater treatment facilities which may be necessary to achieve the discharge effluent limits. 3-2 Discharge to a Sewerage System Discharge of WTPs wastewaters and unthickened sludges to the sewerage system may be feasible when adequate scouring velocity can be maintained in the sewers and the solids loading can be accommodated by the sewage treatment plant. Another limitation to this type disposal is that the sludge digestors used at sewage treatment plants may collect inert materials from WTP waste which may reduce their efficiency , however, the sewage treatment plant may require some degree of pretreatment (such as flow equalization) prior to discharge into its sewer system which would tend to offset the savings realized by this method of disposal. 3-3 Agricultural Land This method requires an evaluation of the effects of these residuals on soils physical properties (cohesion, aggregation, strength and texture which affect hydraulic properties of the soil), on plant growth and on groundwater quality (Elliott et al. 1990). It is worth noticing that alum and iron hydroxides present in the sludge, favour the fixation of available phosphorus (PO4-3), thus making it less easily available to vegetation. This can be negative, and can be contrasted adding phosphorous to the sludge, but can be positive, if the soil is subjected to an excessive phosphorous load due - for example - to the spreading of farm organic wastes. 3-4 Waste Land Filling Conditioned and dewatered sludges may be disposed to public , private lands and to a land owned by the utility. The operation should be controlled with adequate provisions to guard against water or soil pollution resulting from high loading rates and surface runoff. The use of land application most likely create a significant economic burden on a water purveyor or industry. The disposing of drinking sludges in land filling purposes after dewatering can be a costly practice due to the transport cost of sludge to land filling locations. 3-5 Coagulant Recovery and Reuse Due to the cost of alum and other chemicals, a few of the large facilities are pumping the thickened sludge to chemical recovery systems. Several methods have been used for recovery of coagulants. Generally, the
96
methods of recovery of coagulants from water works sludge are acidification, alkalization, ion exchange and membrane separation. However, a combination of these methods may be used to achieve a higher recovery. Although it is expected that recovering and reusing coagulants embedded in the waterworks sludge matrix would: (1) significantly reduce the cost of coagulants used in water and wastewater treatment plants; (2) possibly help to meet discharge standards in certain cases and at reduced cost ; (3) reduce sludge volume and hence disposal costs; (4) make the waterworks sludge more suitable for land filling without concerns over possible metal accumulation and leaching effects; (5) improve the dewatering characteristics of the residual sludge and (6) increase the life of waste disposal facilities, such coagulant recovery process can be extremely complicate. 3-5-1 Acidification Method In this method, alum is recovered by acidulating the sludge with sulphuric acid . This acidification process(Aci) will separate particulate solids and aluminium sulphate(alum). Aluminium sulphate (alum) will be recovered in aqueous solution, while particulate solids will remain in solid form. The acidification process is generally more convenient and more efficient than the basification process for recovering aluminum from sludge. Additionally, the size of the sludge particles and the dissolution temperature are important factors that influence the dissolution of metal ions. When the sludge particles are small, the ability to form complex bonds between the sludge particles and the metal ions is poor. Such weak bonds may promote the dissolution of the sludge. Additionally, a higher dissolution temperature provides sufficient energy to break the amorphous chemical bonds of the sludge and increase the metal dissolution rate. Accordingly, during the acidification process, increasing the solution temperature may increase the quantity of aluminum dissolved. Figure(3) shows flow chart of recycle of alum recovered from water treatment sludge in chemically enhanced primary treatment.
97
Figure(3) Acidification Method for Alum Recovery Source: Ogbonna O, Jimoh WL, Awagu EF, Bamishaye EI,2011.
The acidic alum recovery process presents a potential problem, however, in that it may concentrate certain impurities from soluble metals (i.e. Fe, Mn, Cr, etc.) and the sulfuric acid in the recovered alum. The use of such systems is more practical one in larger facilities (usually greater than 100m3/day). 3-5-2 Alkalization Method As an alternative to acidification, the amphoteric nature of aluminum oxide also permits alum recovery from water sludge under alkaline conditions. A report of feasibility of coagulant recycling by alkaline reaction of aluminum hydroxide sludge by using sodium hydroxide (NaOH) and lime Ca(OH)2 at pH usually ranged from 11 to12. However, the alkaline digestion process has the same limitations as the acid digestion process because high amount of natural organic matter are present in the recovered solution. 3-5-3 Ion Exchange and Membrane processes Researches have been carried out on the industrial use of ion exchange membranes for separation processes such as in recycling and in water and wastewater treatment processes In order to improve the quality of the recovered coagulants using acidification process, the acidic leachate may be treated by ion exchange. The first process conceptualization was based on the use of liquid ion exchanger (LIE) which uses organic solvents to recover highly pure concentrated alum from sludge and the second developed much later relied on the use of a composite ion exchanger. However, the technical feasibility of the two proposed solutions was limited by physical thermodynamic and kinetic problems. During the last two decades pressure driven membrane processes namely reverse osmosis (RO), nanofiltration (NF) and Ultrafiltration (UF) have found increased applications in water utilities and chemical industries . These processes have the advantages of ability to remove particles and colloid almost completely, controlling microorganisms and pathogen and low cost. In this process, aluminum ions are selectively sorbed from an aqueous phase onto a composite membrane and there after desorbed, with the release of aluminum ions as the composite membrane is generated in a sulphuric acid solution. However, they are susceptible to fouling because particulate matter or large molecules concentrate on the membrane surface. Membrane fouling causes a
98
decrease in membrane performance due to reduction of permeate flux through the membrane as a result of increased flow resistance due to pore blocking, concentration polarisation and cake formation. Also, composite ion exchange materials are not available in sizes appropriate for large applications and the process is not capable of concentrating alum to high levels and there is always a solvent carryover that requires further treatment. 3-6 Coagulant in Wastewater Treatment While purity and economic considerations have narrowed the applicability of the coagulant recovery option, several attempts have been made and reported on the direct use of waterworks sludge as a coagulant in the treatment of various wastewaters. It was shown that under certain conditions of optimal alum sludge addition, the treatment and final sludge characteristics at the wastewater treatment plant were improved significantly. It was noted that the iron sludge was as efficient as using alum or ferric chloride, and removal was further enhanced when combined with ferric chloride at various doses. As compared with the use of original coagulants, satisfactory removal efficiencies for colours were also reported in the use of waterworks sludge for the treatment of textile wastewaters and various dyestuffs . 3-7 Adsorption of Pollutants in Waste Water Treatment The availability of drinking sludges at a wastewater treatment plant is a beneficial one, as the residual coagulating capacity can be useful to improve the settling effectiveness, to contribute to the phosphorous abatement, to improve the dehydration stage of sludge. In this case, the final biological sludge of the wastewater treatment plant ("biosolids") can be used - mixed with drinking sludge - for agricultural purposes. Currently, the development of cost -effective composite adsorbents from by - products is gaining considerable attention, as a possible alternative to commonly used adsorbents. Waterworks sludge is no exception and so far it has been preliminarily studied as a potential adsorbent for the removal of various pollutants and metals in wastewaters, e.g. lead, Copper and fluoride. The basic idea is that the abundant amorphous aluminium and ferric ions in waterworks sludge can become valuable for phosphorus removal in wastewaters since such ions have been demonstrated to enhance the processes of adsorption and chemical precipitation that aids phosphorus immobilization. It has been reported that the P-adsorption capacity is largely dependent upon the pH of the P-containing solution, being enhanced in the acidic region. 3-8 Co-Conditioning and Dewatering with Sewage Sludge
99
Although attempts at co-discharging waterworks sludge and sewage sludge are not entirely new, the use of waterworks sludge in coconditioning and enhancing sewage sludge treatability remains an attractive option in research and practice. Studies have shown the beneficial effect of waterworks sludge as a co-conditioner in sewage sludge conditioning and dewatering process. The presence of aluminium hydroxide in the sludge enhanced the settling velocity and dewaterability of biological sludge. It was therefore reasoned that the alum sludge acted as a skeleton builder, making the mixed sludge more incompressible and rendering the dewatering process more effective. Such attempts at co-conditioning and co-disposal of wastes have been noted to be economically advantageous and particularly aid in enhancing sludge dewaterability. However, emphasis has always been placed on the likely disadvantages that may occur rather than the potential advantages such attempts offer. In addition, considering the fact that it is unlikely that a water treatment plant would be cited in close proximity to a sewage treatment facility, the cost and economics of sludge transport/haulage might become a potential deciding factor. The capacity, process control capabilities and willingness to accept the sludge are also other important factors . 3-9 Constructed Wetlands Substrate In recent years, constructed wetlands (CWs) have been increasingly used worldwide as a popular alternative technology for the treatment of numerous wastewaters. Due to their low energy requirement and aesthetical appearance, CWs are seen as a green- wastewater treatment technique.The media in CWs play an integral role in various biological, physical and chemical processes that remove pollutants from the wastewater. One of the main objectives of research in wetland technology today is to discover new medium material that will increase the effectiveness and, hopefully reduce the capital cost. Traditionally, different combinations of soil, sand and gravel have been used as media in the wetlands. Numerous studies have shown that the wetlands based on these conventional media are capable of meeting the requirement of BOD5 and COD reductions. However, it is often difficult to achieve substantial removal of certain inorganic nutrients, e.g. orthophosphate and ammoniacal-nitrogen, in wetlands with the conventional media. The possible use of dewatered waterworks sludge as a medium in CWs is thus another prospective option open to active research. It has been suggested that since typical waterworks sludges are rich in aluminium, iron and calcium residues, which are strong adsorbents for poll utants in
100
wastewaters, especially phosphorus, their use in CWs to enhance phosphorus reduction could be a possibility. 3-10 Land Based Applications Land -based application of waterworks sludge is the controlled spreading of the sludge onto or incorporation into the surface layer of soil to stabilize, degrade and immobilize the sludge constituents. Historically, the most notable land application of waterworks sludge is the use of lime softening sludge as a substitute for agricultural limestone. Currently land based applications of waterworks sludges are gaining increasing attention as alternative disposal means. This is most probably hinged on the fact that the physical, chemical and biological properties of soils can be used to assimilate the applied waste without adverse effects on soil quality and even with the possibility of enhancing soil quality. In comparison with land filling option, land based applications are viewed as a low cost and favourable alternative, which may not necessarily require regulatory permits, although considerable land area may be needed. Over the years, the scope of such applications have typically been as a sustainable means to dispose waterworks sludge, improve or reclaim certain soil qualities or used as part of growing medium for crops. The major concern however has been its perception as a metal hydroxide waste, which could have potential deleterious effect on both soil and crop planted. On the basis of this review, three main factors are crucial to the success of the land based applications:(1) Determining the optimum effective application rate with the least consequences (2) The particular nature of the sludge and (3) The exact intent of the application. 3-10-1 Structural Soil Improvement The physico-chemical properties of waterworks sludge makes them suitable for land spreading and in some instances their alkaline property perhaps, have encouraged their use as an ameliorative conditioner for soils, while improving other soil properties. Such approaches may provide an economical disposal means for the sludge while probably serving to improve certain soil qualities and enhance plant growth. In other instances, there have been attempts to reuse waterworks sludge as a source of biofertilizer, although there are concerns over lack of potassium and other nutrients in the sludge, which makes it incomparable with commercial grade fertilizers. However, the limiting metal levels in the waterworks sludge is important for long term land application as it determines the useful life of such application sites on the basis of cumulative metal loadings . 3-10-2 As Soil Buffer 101
Few studies have evaluated the feasibility of utilizing the alkaline properties of waterworks sludge to act as soils pH buffer. Particularly, lime -containing sludge has been used for soil conditioning and pH adjustment. George (1975) , AWWA (1981) and Elliot and Dempsey (1991) have all reported on the soil neutralizing capacity of lime softening sludges and were found to increase soil pH more than limestone. This is in contrast with the reduced soil pH observed and reported in the case of alum sludge derived from municipal wastewater treatment plant, used as a soil amendment in a greenhouse study with barley (Wang et al., 1998). In separate studies using waterworks sludge, Heil and Barbarick (1989) and Dayton and Basta (2001) reported that waterworks sludge may be an effective liming agent and this was corroborated by the findings of Rensburg and Morgenthal (2003), in which waterworks sludge was effectively used as an ameliorant for acid -generating mine tailings. Elliot and Dempsey (1991) however noted that most coagulation sludges have limited ability to serve as agricultural liming materials because their calcium carbonate equivalence (CCE) generally range from 10-20% of commercial limestone, in contrast to the CCE of lime softening sludges which is typically 80-103%. Coagulant sludges have highly varied plant nutrients and a comparatively low CCE values and these have limited their use as soil stabilizers or conditioners (Elliot and Singer, 1988). 3-10-3 Nutrient Reduction in Laden Soils and Runoffs Application of manures and biosolids to improve soil quality is a well known agricultural practice. Unfortunately, long term application of such soil amendments often lead to soil nutrients level (P and N) in excess of crop needs and thus becomes a potential source of incidental nutrient leak to water bodies, which is not desirable. Sharpley et al. (1994) noted that application of animal manure in amounts that exceed agronomic rates based on the N requirement for crop production often results in increased loss of P from agricultural lands in surface run offs and potential eutrophication of surface waters. Poultry litter, when used as an inexpensive fertilizer source to improve soil quality has also been shown to increase NH4 concentration in addition to P in surface run offs (Liu et al., 1997; Sharpley, 1997; Gallimore et al. 1999).This has led to the use of chemical amendments to nutrients in soils and biosolids applied to land, and run offs from such lands/soils, such as the use of aluminium, iron and calcium salts to decrease P solubility in poultry manure and runoff from manure -amended soils (Moore and Miller, 1994). However, since waterworks sludge contain hydrous oxides with substantial P-fixing capacity (Elliot et al., 1990), they have been utilized
102
as a low-cost alternative and chemical based best management practice to remediate phosphorus laden soils and prevent phosphorus loss in runoffs, especially from agricultural lands. In this context, concerns have also been expressed over the potential phytotoxicity of inorganic aluminium and fixation of plant available phosphorus. It is therefore desirable that efforts at using waterworks sludge to attenuate phosphorus pollution should include retrospectively, an evaluation and assessment of these potential negative impacts . 3-11 Use of Sludges as Building and Construction Materials Waterworks sludges have also been preliminarily studied and used as building and construction materials. However, despite the obvious advantages and increasing researches into the incorporation of waterworks sludges in building and construction materials, they are yet to be fully accepted in the industry. Of particular concern is the variability in the final product made from such sludges due to the variability in their chemical composition and water and organic content, even when such products wholly conform to industry standards . In other words, for sludge products to become fully integrated into the industry, they must be seen to be reliable, with a high degree of compositional stability to make them cost effective and justify their use. 3-11-1 Brick Making Due to the similar mineralogical composition of clay and WTP sludge, the complete substitution of brick clay by sludge incorporated with some of the agricultural and industrial wastes, such as rice husk ash (RHA) and silica fume (SF). Three different series of sludge to SF to RHA proportions by weight can be mixed and fired at a relatively high temperature. The physical and mechanical properties of the produced bricks were then determined, evaluated and compared to control claybrick. In Egypt, similar studies investigated the use of sludge as a complete or partial substitute for clay in brick manufacturing (Hegazy 2007,Hassanain 2008, Ramadan et al. 2008). In this trend, different series of sludge and clay proportioning ratios were tried, which involved the addition of sludge with ratios between 50 and100% by weight. Each series was fired at different temperatures between 950 and 1100oC. The physical properties of the produced brick were then determined and evaluated according to Egyptian Standard Specifications (E.S.S). 3-11-2 Manufacture of cement and cementitious materials Generally , recycling of waterworks sludge in the cement industry can be a practical alternative as reported by Pan et al. (2004), in that the waterworks sludge is virtually non -hazardous, and the chemical 103
composition of the inorganic sludge is similar to the clay used in cement production. In their report, fresh waterworks sludge was successfully incorporated in the making of Portland cement through the sintering process. It was reported that the addition of the waterworks sludge in the cement clinker increased the compressive strength of the concrete and benefited the clinker burnability, without any detrimental effect on the long -term strength property. Setting times and soundness test results were equally satisfactory. However, it was noted that the preferred waterworks sludge should have a considerable low chlorine level as it has been noted that chlorine could corrode the cement kiln and block its duct (Kikuchi, 2001). The chlorine level in the waterworks sludge used was 335.5ppm.In addition, Carvalho and Antas (2005) in a review of studies on sludge incorporation into cement noted the following: (1) during drying at 105 oC, sludge suffered agglomeration and had to be grind before use; (2) sludge dewatered or heated at 105oC prevents the setting and hardening of paste and mortar; (3) thermally treated sludge decreases the compressive strength of mortar, but promotes the increase of it's consistency; (4) compressive strength decreased with an increase in sludge content and treatment temperature and (5) sludge treated at 700 oC induced the formation of lime and calcium aluminates, which might have caused the observed decrease of initial setting time. It was therefore concluded that sludge incorporation into mortar cement could only be feasible at temeperatures above 450°c, with an increase of the initial setting time but a decrease of the mechanical strength. 3-11-3 Use in Pavement and Geotechnical Works Although still in the preliminary stage and yet to be widely studied and reported, the possibility of using waterworks sludge as geotechnical works material (e.g. waste containment barriers, soil modelling, structural fills) and incorporation into construction materials (bituminous mixtures, subbase material for road construction) and as landfill liner have been reported (Ronald and Donald, 1977; Raghu, et al 1987; Carvalho and Antas, 2005). This is particularly based on preliminary characterization test results on the geotechnical and geoenvironmental characteristics of waterworks sludge which shows some promise as a suitable geotechnical and construction material. Carvalho and Antas (2005) reviewed the feasibility of sludge incorporation as a filler material in bituminous mixtures for use in general pavement works. It was recommended that sludge should be thermally treated to at least a temperature of 450oC to volatize all the organic component s. Such thermally dried sludge suffered agglomeration and needed to be grind before use. However, the dried and grind sludge had heterogenic
104
granulometria which was incompatible with fillers granulometria range. Therefore, the need to eliminate organics in the sludge may lead to incompatibility between the sludge and traditional filler material. Consequently, an optimum temperature that would maximize sludge organic removal and minimize incompatibility with traditional fillers is desirable. However, such thermal treatment may present some environmental problems, as there are concerns over malodorous emissions during the thermal drying. Obviously, such odorous emissions may limit large-scale industrial application of the process.
105
Appendix (2): In-stream Wetland System 1-Background Treatment wetlands are natural or constructed wetlands engineered for water quality improvement. Over the past five decades, treatment wetlands have grown to become an accepted technology for improving water quality with minimal energy requirements and a more natural, “environmentally-friendly” profile. Historically, wetlands have been most frequently used for removal of conventional wastewater contaminants, such as BOD, TSS, and nutrients. More recently, treatment wetland technologies have been applied to a diversity of polluted waters.
2- Constructed wetlands: types and classifications There are many other possibilities to classify wetland systems. The systematic classification approach distinguishes between the surface flow and the subsurface flow constructed wetlands whereas the surface flow systems are the older technology. The subsurface flow systems are further divided into horizontal flow and vertical flow systems.
2-1 Surface flow treatment wetlands Surface flow or free-water-surface wetland systems show, as the name suggests, a water flow primarily conducted aboveground and exposed to the atmosphere (free water body). Re-aeration at the surface is the major
106
oxygen source in this wetland type. Below the free water body, the bed
contains a soil layer which serves as a rooting media for the emergent vegetation. At the bottom of the wetland system, an impermeable barrier (liner or native soil) is required to avoid infiltration, and thus, contamination of groundwater (Figure 1). Figure (1): Surface flow constructed wetland
2-2 Subsurface flow treatment wetlands Subsurface flow treatment wetlands can be divided into soil- and gravelbased wetlands on the one hand, and into horizontal and vertical flow systems on the other hand. In the following chapters the different types and developments are briefly introduced. 2.2.1 Horizontal flow systems Main feature of horizontal flow systems is that the water level remains underneath the ground surface. The wastewater flows horizontally through a porous soil medium where the emergent plant vegetation is rooted, and is purified during the contact with the surface areas of the soil particles and the roots of the plants. This system includes an impermeable liner or native soil material at the bottom to prevent possible contamination of the groundwater (Figure.2).
107
Figure (2): Horizontal subsurface flow wetland In contrast to the surface flow wetlands, the soil contributes to the treatment processes by providing a surface area for microbial growth and supporting adsorption and filtration processes. This effect results in a lower area demand and generally higher treatment performance per area than free-water-surface wetlands 2-3 Vertical flow systems Vertical flow systems are characterized by an intermittent (discontinuous) charging including filling and resting periods where wastewater percolates vertically through a soil layer that consists of sand, gravel or a mix of these, (figure 3). Key advantage of vertical flow systems is an improved oxygen transfer into the soil layer. Beside oxygen input by the plants and diffusion processes that both occur also in horizontal subsurface flow wetlands, vertical flow filter show a significant oxygen input into the soil through convection caused by the drainage. Compared to horizontal subsurface flow systems, the additional aeration of the soil by convective processes allows higher nitrification capacities as well as removal of organic matter. However, denitrification that requires anoxic conditions is usually lower in vertical flow beds compared to horizontal subsurface flow beds. They are also less effective for removal of suspended solids than horizontal surface flow and subsurface flow beds.
Figure (3): Vertical flow constructed wetland
108
3-Preliminary Estimates of Wetland Area Needed for Treatment The approximate magnitude of wetland acres corresponding to various levels of treatment performance were estimated using the first order area-based treatment wetland model based on:-
Where: A = wetland area (square meters [m2]) Q = flow (cubic meters per year [m3/yr]) Ci = inflow (influent) concentration (mg/L) Ce = wetland outflow (effluent) concentration (mg/L) C* = wetland equilibrium background concentration (mg/L) k = first‐order, area‐based rate constant (m/yr), which varies depending upon pollutant P = weathering factor that takes into account the estimated number of hydraulic tanks‐in‐series and the number of component compounds for a particular parameter (dimensionless) In this model, the value of k can vary for temperature‐dependent parameters, such as N, and is adjusted using the Arrhenius equation as follows: Where: = temperature coefficient T = temperature (degree Celsius [°C])
4-Comparison of horizontal surface and subsurface flow wetlands The advantages and disadvantages of these two wetland types are not commonly valid because they depend on the specific situation and site conditions. However, some general major aspects could be stated:• Horizontal surface flow wetlands Advantages: - Lower installation costs (can be offset by the greater surface area needed) - Lower operation and maintenance costs
109
- Simpler hydraulics -Open water areas (provides wildlife habitat including a high biodiversity) - High effectiveness in removal of suspended solids (TSS) Disadvantages: - Lower removal efficiency per area (more area required) - Lower cold tolerance (more suitable in warmer climates) - Potential problems with odors and mosquito populations - Wastewater is exposed to potential human contact - Higher evapotranspiration rates (increases pollutant concentrations) (Especially in warmer climates like the Mediterranean region) • Horizontal subsurface flow wetlands Advantages: - Higher removal efficiency per area (less area required) - Higher cold tolerance because of insulation through the upper media layer (More suitable in cold/boreal and temperate climate zones) - No or minimal odor and mosquito problems (if subsurface flow is maintained) Disadvantages: - Higher construction costs, mainly caused by the substrate media (Can be offset by less area required) - Higher operation and maintenance costs - More sensitive to elevated concentrations of suspended solids (clogging effects at the inlet zone) This comparison shows that subsurface flow wetlands are more effective in removal of wastewater pollutants whereas less area is required. They have also higher climatic tolerance ranges making their use in nearly all climate zones and regions worldwide possible. Furthermore, the variety of suitable emergent plant species that can be chosen appears to be higher compared to the surface flow wetlands . Horizontal and vertical subsurface flow wetlands can be considered as a more efficient system type and they are to be preferred to the surface flow systems due to their generally higher performance.
110
Appendix (3) :MCA Benchmark Indicators,( Ellis, 2000) Primary Criteria and Indicators
Technical Benchmark Indicators
Performance and durability
Flexibility and adaptability
Resources availability
Sustainability
Environmental Benchmark Indicators
Protection of surface water
Protection of ground water
Protection of land stability Protection of river habitat
Social and Community Benchmark Indicators
Economical Benchmark Indicators
Resources use
Initial cost
Benchmark Standards -Operational lifetime - Pollutant concentration probability exceedance for given target levels - %age compliance with consent/receiving water - Likelihood of system failure; alarm/intervention procedures - Safety level/provision for accidental pollution - Ability to monitor - Design freeboard for storage and water quality change - Ease of retrofitting and modification - Costs of retrofitting and add-on structures/features - Use of nature resource - Visual impact on resource - Reliability of sources quantities - Keeping the quantity and quality of the sources for next generation - Compliance with current water management strategy - Political stability of the source country - Compatibility with international laws and existing agreements - Reliability of relevant institutions - Treatment retention times - Receiving water classification - Compliance with surface water quality standards - Low flow status -Variation of water table level - Ground water vulnerability to future rain water use - Impact on aquifer balance -Impact on aquifer quality -Variation of soil permeability - Impact on agriculture area -Reduction of contaminants introduced into aquatic ecosystems - Land take (area/cost) - Number (and/or cost) of development units/space lost - Aggregate/concrete/top-soil/appurtenances use and costs - Energy consumption -Quantitative parameters for costs - Investment costs (Land acquisition, building, equipments) - Feasibility investment studies and all other intangible assets
Operation/ Maintenance cost
- Need and frequency for O & M servicing to maintain technical/ environmental/amenity/habitat objectives
Cost of loss investments
- Opportunity cost of water
Health and safety risks
- Local community concerns (injury, infection, drowning,.. etc) - Formal technical risk exposure audit (flood risk, health risk, safety risk, habitat creation,.. etc )
Stakeholders acceptability
-Fulfillment of the development needs. - Perception of environmental benefits/risks
111
Appendix (4): Sample of Microsoft Visual C codes
(A)The Main Interface
112
(B)The Login Process
(B-1)Login Code
113
(C) Main Form Codes namespace DataBase public partial class Form1 : Form { Login login; public Form1(Login l) { InitializeComponent(); login = l; private void cairoReachMapToolStripMenuItem_Click(object sender, EventArgs e) { Cairo_Reach_Map CRM = new Cairo_Reach_Map(); CRM.MdiParent = this; CRM.Show(); private void drinkingWaterPlantsToolStripMenuItem1_Click(obje ct sender, EventArgs e) { DrinkingWaterPlantsLocations CRM = new DrinkingWaterPlantsLocations(); CRM.MdiParent = this; CRM.Show(); } private void drinkingWaterPlantsToolStripMenuItem2_Click(obje ct sender, EventArgs e) private void fostatToolStripMenuItem_Click(object sender, EventArgs e) { 114
Fostat CRM = new Fostat(); CRM.MdiParent = this; CRM.Show(); private void kafrElElwDWPToolStripMenuItem_Click(object sender, EventArgs e) KafrElElw CRM = new KafrElElw(); CRM.MdiParent = this; CRM.Show();
(D) Code for Cairo reach discharge ( Cairo Reach Module)
namespace DataBase.Cairo_Reach_Module { public partial class CairoReachDischarge2012 : Form { public CairoReachDischarge2012() { InitializeComponent(); GetExcel(); } rptds ds = new rptds();
115
private DataTable GetDataTable(string sql, string connectionString) { DataTable dt=new DataTable(); using (OleDbConnection conn = new OleDbConnection(connectionString)) { conn.Open(); using (OleDbCommand cmd = new OleDbCommand(sql, conn)) { using (OleDbDataReader rdr = cmd.ExecuteReader()) dt.Load(rdr); return dt; private void GetExcel() { string fullPathToExcel = "E:/DataBase/Cairo Reach Module/Cairo Reach Discharge/Discharge 2012.xls"; string connString = string.Format("Provider=Microsoft.ACE.OLEDB.12.0 ;Data Source={0};Extended Properties='Excel 12.0;HDR=yes'", fullPathToExcel); DataTable dt = GetDataTable("SELECT * from [Discharge 2012$]", connString); for (int i = 0; i < dt.Rows.Count;i++ ) { //Do what you need to do with your data here if (i == 2) ds.Tables["Chart"].Clear(); ds.Tables["Chart"].Rows.Add("January", dt.Rows[i][1].ToString());
116
ds.Tables["Chart"].Rows.Add("Febraury", dt.Rows[i][2].ToString()); ds.Tables["Chart"].Rows.Add("March", dt.Rows[i][3].ToString()); ds.Tables["Chart"].Rows.Add("April", dt.Rows[i][4].ToString()); ds.Tables["Chart"].Rows.Add("May", dt.Rows[i][5].ToString()); ds.Tables["Chart"].Rows.Add("June", dt.Rows[i][6].ToString()); ds.Tables["Chart"].Rows.Add("July", dt.Rows[i][7].ToString()); ds.Tables["Chart"].Rows.Add("August", dt.Rows[i][8].ToString()); ds.Tables["Chart"].Rows.Add("September", dt.Rows[i][9].ToString()); ds.Tables["Chart"].Rows.Add("October", dt.Rows[i][10].ToString()); ds.Tables["Chart"].Rows.Add("November", dt.Rows[i][11].ToString()); ds.Tables["Chart"].Rows.Add("December", dt.Rows[i][12].ToString()); Chart rpt = new Chart(); rpt.SetDataSource(ds.Tables["Chart"]); crystalReportViewer1.ReportSource = rpt; dataGridView1.Rows.Add();
117
dataGridView1[0, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][0].ToString(); dataGridView1[1, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][1].ToString(); dataGridView1[2, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][2].ToString(); dataGridView1[3, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][3].ToString(); dataGridView1[4, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][4].ToString(); dataGridView1[5, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][5].ToString(); dataGridView1[6, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][6].ToString(); dataGridView1[7, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][7].ToString(); dataGridView1[8, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][8].ToString(); dataGridView1[9, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][9].ToString(); dataGridView1[10, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][10].ToString(); dataGridView1[11, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][11].ToString(); dataGridView1[12, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][12].ToString();
118
private void CairoReachDischarge2012_Load(object sender, EventArgs e) private void reportViewer1_Load(object sender, EventArgs e) private void button2_Click(object sender, EventArgs e) { frmReportPreview frmrv = new frmReportPreview(); rptMonthWithChart rpt = new rptMonthWithChart(); rpt.SetDataSource(ds.Tables["Chart"]); frmrv.crystalReportViewer1.ReportSource = rpt; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text20"] ).Text = ds.Tables["Chart"].Rows[0][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text21"] ).Text = ds.Tables["Chart"].Rows[1][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text22"] ).Text = ds.Tables["Chart"].Rows[2][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text23"] ).Text = ds.Tables["Chart"].Rows[3][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text24"]
119
).Text = ds.Tables["Chart"].Rows[4][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text25"] ).Text = ds.Tables["Chart"].Rows[5][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text26"] ).Text = ds.Tables["Chart"].Rows[6][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text27"] ).Text = ds.Tables["Chart"].Rows[7][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text28"] ).Text = ds.Tables["Chart"].Rows[8][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text29"] ).Text = ds.Tables["Chart"].Rows[9][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text30"] ).Text = ds.Tables["Chart"].Rows[10][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text31"] ).Text = ds.Tables["Chart"].Rows[11][1].ToString(); ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text32"] ).Text = "Cairo Reach Discharge (2012)";
120
(E) Code for wastewater from drinking plant 2012 (Pollution Source Module)
namespace DataBase.Cairo_Reach_Module { public partial class wastewaterfromDtinkingplant2012 : Form { dsReport ds = new dsReport(); public wastewaterfromDtinkingplant2012() { InitializeComponent(); GetExcel(); } private DataTable GetDataTable(string sql, string connectionString) { DataTable dt=new DataTable(); using (OleDbConnection conn = new OleDbConnection(connectionString)) {
121
conn.Open(); using (OleDbCommand cmd = new OleDbCommand(sql, conn)) { using (OleDbDataReader rdr = cmd.ExecuteReader()) { dt.Load(rdr); return dt; private void GetExcel() { string fullPathToExcel = "E:/DataBase/Pollution Sources/wastewater from Dtinking plant/DWP Wastewater.xls"; //ie C:\Temp\YourExcel.xls string connString = string.Format("Provider=Microsoft.ACE.OLEDB.12.0 ;Data Source={0};Extended Properties='Excel 12.0;HDR=yes'", fullPathToExcel); DataTable dt = GetDataTable("SELECT * from [2012$]", connString); for (int i = 0; i < dt.Rows.Count;i++ ) { if (i != 0) try { ds.Tables["SecondSample"].Rows.Add(dt.Rows[i][0] .ToString(), dt.Rows[i]["F2"].ToString(), dt.Rows[i]["F3"].ToString(), dt.Rows[i]["F4"].ToString(), dt.Rows[i]["F5"].ToString(), dt.Rows[i]["F6"].ToString(), dt.Rows[i]["F7"].ToString()); dataGridView1.Rows.Add(); dataGridView1[0, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][0].ToString();
122
dataGridView1[1, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][1].ToString(); dataGridView1[2, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][2].ToString(); dataGridView1[3, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][3].ToString(); dataGridView1[4, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][4].ToString(); dataGridView1[5, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][5].ToString(); dataGridView1[6, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][6].ToString(); } catch { dataGridView1.Rows.Add(); dataGridView1[0, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][1].ToString(); dataGridView1[1, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][2].ToString(); dataGridView1[2, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][3].ToString(); dataGridView1[3, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][4].ToString(); dataGridView1[4, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][5].ToString(); dataGridView1[5, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][6].ToString();
123
dataGridView1[6, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][7].ToString(); ds.Tables["SecondSample"].Rows.Add(dt.Rows[i][1] .ToString(), dt.Rows[i][2].ToString(), dt.Rows[i][3].ToString(), dt.Rows[i][4].ToString(), dt.Rows[i][5].ToString(), dt.Rows[i][6].ToString(), dt.Rows[i][7].ToString()); private void button2_Click(object sender, EventArgs e) { frmReportPreview frmrv = new frmReportPreview(); rptSecondSample rpt = new rptSecondSample(); rpt.SetDataSource(ds.Tables["SecondSample"]); frmrv.crystalReportViewer1.ReportSource = rpt; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text1"]) .Text = "Drinking Water Plant "; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text8"]) .Text = "Drinking Water Plants Characteristics (2012)"; frmrv.Show();
(F) Code for Iron 2014 (water Quality Module)
124
using System; using System.Collections.Generic; using System.ComponentModel; using System.Data; using System.Drawing; using System.Text; using System.Windows.Forms; using System.Data.OleDb; using DataBase.Cairo_Reach_Module.Cairo_Reach_Discharg e; using DataBase.Water_Quality_Data.Water_Quality_Parame ters.WQ_2013; using DataBase.Reports; using DataBase.Reports.rpt; namespace DataBase.Cairo_Reach_Module { public partial class WQ2014Iron : Form { dsReport dsRep = new dsReport(); public WQ2014Iron() { InitializeComponent(); GetExcel(); } private DataTable GetDataTable(string sql, string connectionString) { DataTable dt=new DataTable();
125
using (OleDbConnection conn = new OleDbConnection(connectionString)) { conn.Open(); using (OleDbCommand cmd = new OleDbCommand(sql, conn)) { using (OleDbDataReader rdr = cmd.ExecuteReader()) dt.Load(rdr); return dt; private void GetExcel() string fullPathToExcel = "E:/DataBase/Water Quality Data/Water Quality Parameters/WQ 2014/WQ_2014.xls"; string connString = string.Format("Provider=Microsoft.ACE.OLEDB.12.0 ;Data Source={0};Extended Properties='Excel 12.0;HDR=yes'", fullPathToExcel); DataTable dt = GetDataTable("SELECT * from [WQ (2014)$]", connString); rptds ds = new rptds(); ds.Tables["Chart"].Clear(); for (int i = 0; i < dt.Rows.Count;i++ ) { //Do what you need to do with your data here if (i > 0) if (dt.Rows[i][0].ToString() != "") ds.Tables["Chart"].Rows.Add(dt.Rows[i][2].ToStri ng(), dt.Rows[i][9].ToString());
dataGridView1.Rows.Add();
126
dataGridView1[0, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][0].ToString(); dataGridView1[1, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][1].ToString(); dataGridView1[2, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][2].ToString(); dataGridView1[3, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][9].ToString(); dsRep.Tables["ThirdSample"].Rows.Add(int.Parse(d t.Rows[i][0].ToString()), dt.Rows[i][1].ToString(), double.Parse(dt.Rows[i][2].ToString()), double.Parse(dt.Rows[i][9].ToString())); Iron rpt = new Iron(); rpt.SetDataSource(ds.Tables["Chart"]); crystalReportViewer1.ReportSource = rpt; private void CairoReachDischarge2012_Load(object sender, EventArgs e) private void reportViewer1_Load(object sender, EventArgs e)
private void button2_Click(object sender, EventArgs e) { frmReportPreview frmrv = new frmReportPreview(); CrystalReport rpt = new CrystalReport(); rpt.SetDataSource(dsRep.Tables["ThirdSample"]); frmrv.crystalReportViewer1.ReportSource = rpt;
127
((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text4"]) .Text = "Iron"; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text5"]) .Text = "Iron"; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text7"]) .Text = "Water Quality Parameters (Iron) 2014"; frmrv.Show();
(G) Code for calibration (Modeling Results)
using using using using using using using using
System; System.Collections.Generic; System.ComponentModel; System.Data; System.Drawing; System.Text; System.Windows.Forms; System.Data.OleDb; 128
using DataBase.Cairo_Reach_Module.Cairo_Reach_Discharg e; using DataBase.Water_Quality_Data.Water_Quality_Parame ters.WQ_2013; using DataBase.Water_Quality_Data.WQI; using DataBase.Reports; using DataBase.Reports.rpt; using DataBase.Mike11.Model_Calibaration_Results; namespace DataBase.Cairo_Reach_Module { public partial class WaterQualityDataset2012 : Form { dsReport dsRep = new dsReport(); public WaterQualityDataset2012() { InitializeComponent(); GetExcel(); } private DataTable GetDataTable(string sql, string connectionString) { DataTable dt=new DataTable(); using (OleDbConnection conn = new OleDbConnection(connectionString)) { conn.Open(); using (OleDbCommand cmd = new OleDbCommand(sql, conn)) { using (OleDbDataReader rdr = cmd.ExecuteReader()) dt.Load(rdr); return dt; private void GetExcel() {
129
string fullPathToExcel = "E:/DataBase/MIKE11 Modeling/Model Calibaration Results/Water Quality Dataset2012.xls"; string connString = string.Format("Provider=Microsoft.ACE.OLEDB.12.0 ;Data Source={0};Extended Properties='Excel 12.0;HDR=yes'", fullPathToExcel); DataTable dt = GetDataTable("SELECT * from [2012$]", connString); rptds ds = new rptds(); ds.Tables["Scenario"].Clear(); for (int i = 0; i < dt.Rows.Count;i++ ) { //Do what you need to do with your data here if (i > 0) if (dt.Rows[i][0].ToString() != "") ds.Tables["Scenario"].Rows.Add(dt.Rows[i][4].ToS tring(), dt.Rows[i][2].ToString(), dt.Rows[i][3].ToString()); dataGridView1.Rows.Add(); dataGridView1[0, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][0].ToString(); dataGridView1[1, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][1].ToString(); dataGridView1[2, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][2].ToString(); dataGridView1[3, dataGridView1.Rows.Count - 1].Value = dt.Rows[i][3].ToString(); } } rptWaterQualityDataset2012 rpt = new rptWaterQualityDataset2012();
130
rpt.SetDataSource(ds.Tables["Scenario"]); crystalReportViewer1.ReportSource = rpt; private void CairoReachDischarge2012_Load(object sender, EventArgs e) private void reportViewer1_Load(object sender, EventArgs e)
private void button2_Click(object sender, EventArgs e) { frmReportPreview frmrv = new frmReportPreview(); CrystalReport rpt = new CrystalReport(); rpt.SetDataSource(dsRep.Tables["ThirdSample"]); frmrv.crystalReportViewer1.ReportSource = rpt; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text4"]) .Text = "WQI"; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text5"]) .Text = "WQI"; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text7"]) .Text = "WQI 2013"; frmrv.Show(); private void dataGridView1_CellContentClick(object sender, DataGridViewCellEventArgs e)
131
(H) Technical Reports (Management Scenarios – Scenarios 1 - BOD )
public void TechnicalReport3(string path, string path2, string DesPath, string ImageName1, string ImageName2, string ExcelPath, string SelectCom, string Text, string ColName1, string ColName2) { string connString = string.Format("Provider=Microsoft.ACE.OLEDB.12.0 ;Data Source={0};Extended Properties='Excel 12.0;HDR=yes'", ExcelPath); DataTable dt = GetDataTable(SelectCom, connString); dsReport ds = new dsReport();
132
ds.Tables["TechnicalReport1"].Clear(); for (int i = 0; i < dt.Rows.Count; i++) //Do what you need to do with your data here if (i > 0) if (dt.Rows[i][0].ToString() != "") ds.Tables["TechnicalReport1"].Rows.Add(dt.Rows[i ][0].ToString(), dt.Rows[i][1].ToString(), double.Parse(dt.Rows[i][2].ToString()), 0, 0); DataTable img = new DataTable(); DataRow drow; img.Columns.Add("Image", System.Type.GetType("System.Byte[]")); drow = img.NewRow(); FileStream fs; BinaryReader br; if (File.Exists(path)) { File.Copy(path, DesPath + ImageName1, true);
fs = new FileStream(DesPath + ImageName1, FileMode.Open); } else File.Copy(path, DesPath + ImageName1, true); fs = new FileStream(DesPath, FileMode.Open);
133
br = new BinaryReader(fs); byte[] imgbyte = new byte[fs.Length + 1]; imgbyte = br.ReadBytes(Convert.ToInt32((fs.Length))); drow[0] = imgbyte; img.Rows.Add(drow); br.Close(); fs.Close(); frmReportPreview frmrv = new frmReportPreview(); rptTechnicalReport3 rpt = new rptTechnicalReport3(); rpt.SetDataSource(ds.Tables["TechnicalReport1"]) ; rpt.Subreports[0].SetDataSource(img); img.Clear(); if (File.Exists(path2)) { File.Copy(path2, DesPath + ImageName2, true);
fs = new FileStream(DesPath + ImageName2, FileMode.Open); } else File.Copy(path2, DesPath + ImageName2, true); fs = new FileStream(DesPath, FileMode.Open);
134
br = new BinaryReader(fs); byte[] imgbyte2 = new byte[fs.Length + 1]; imgbyte2 = br.ReadBytes(Convert.ToInt32((fs.Length))); drow[0] = imgbyte2; img.Rows.Add(drow); br.Close(); fs.Close(); rpt.Subreports[1].SetDataSource(img);
frmrv.crystalReportViewer1.ReportSource = rpt; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text6"]) .Text = Text; ((CrystalDecisions.CrystalReports.Engine.TextObj ect)rpt.ReportDefinition.ReportObjects["Text7"]) .Text = ColName1; frmrv.Show(); } private void toolStripMenuItem28_Click(object sender, EventArgs e) { TechnicalReport("E:/DataBase/MIKE11 Modeling/Management Scenarios/Senario(1)_ wetland/Scenario (1)_BOD/Scenario(1) _BOD.jpg", "E:/DataBase/MIKE11 Modeling/Management Scenarios/Senario(1)_ wetland/Scenario (1)_BOD/SCENARIO(1)_BOD_GIS.jpg", "E:/DataBase/PrintImage", "/Scenario(1) _BOD.jpg", "/SCENARIO(1)_BOD_GIS.jpg",
135
"E:/DataBase/MIKE11 Modeling/Management Scenarios/Senario(1)_ wetland/Scenario (1)_BOD/Scenario(1)_BOD.xls", "SELECT * from [BOD-Senario Wetland$]", "Scenario (1) Treatment of Four Polluted Drains Using Wetland Technique BOD(mg/l)","BOD");
(I) The Drawing Process
(I-1)The Reading Function
(I-2) Writing Function
136
(I-3)Codes for Draw Function
(J) Load Image Code public MK11Image(string imagelink,bool print) InitializeComponent(); img = imagelink; privatevoid MK11Image_Load(object sender, EventArgs e)
137
pictureBox1.Image = Image.FromFile(img); string data = img;
(K) Management Scenarios Process
void drawAll() { foreach (var series in chart1.Series) { series.Points.Clear(); chart1.Series.Clear(); chart1.Titles.Clear(); string[] seriesArray = { "Mean BOD", "Mean COD", "Mean FC" }; chart1.Titles.Add("Scenarios Comparesion"); chart1.ChartAreas[0].AxisY.Interval =5; chart1.ChartAreas[0].AxisY.Minimum = 0; chart1.ChartAreas[0].AxisY.Maximum = 45; chart1.ChartAreas[0].AxisY.Title = "Reduction Percent"; chart1.ChartAreas[0].AxisX.Interval = 1; chart1.ChartAreas[0].AxisX.Title = "Scenarios"; chart1.ChartAreas[0].AxisY.LabelAutoFitStyle = LabelAutoFitStyles.None; chart1.ChartAreas[0].AxisY.TitleFont = new System.Drawing.Font("Trebuchet MS", 12, System.Drawing.FontStyle.Bold); for (int i = 0; i < seriesArray.Length; i++) string text = ""; if (i == 0) string s = newPath.Replace("X", "B"); text = System.IO.File.ReadAllText(s); elseif (i == 1) 138
string s = newPath.Replace("X", "C"); text = System.IO.File.ReadAllText(s); elseif (i == 2) string s = newPath.Replace("X", "F"); text = System.IO.File.ReadAllText(s); string[] dat = text.Split(';'); for (int ii = 0; ii < len; ii++) pointsArray[ii] = Convert.ToDouble(dat[ii]); Series series = this.chart1.Series.Add(seriesArray[i]); chart1.Series[i].Color = colors[i]; for (int j = 0; j < pointsArray.Length; j++) series.Points.AddXY(monthArray[j], pointsArray[j]); if (x == 0) series.ChartType = SeriesChartType.Line; series.BorderWidth = 3; privatevoid Mk11Gen3_Load(object sender, EventArgs e) dataGridView1.Rows[0].HeaderCell.Value = "Mean BOD"; dataGridView1.Rows[1].HeaderCell.Value = "Mean COD"; dataGridView1.Rows[2].HeaderCell.Value = "Mean FC";
(L) MCA Scenarios Process void drawAll() foreach (var series in chart1.Series) series.Points.Clear(); 139
chart1.Series.Clear(); chart1.Titles.Clear(); string[] seriesArray = { "Technical", "Environmental", "Economical", "Social & Community", "Scenario total weight score" }; chart1.Titles.Add("MCA Scenario"); chart1.ChartAreas[0].AxisY.Interval = 0.1; chart1.ChartAreas[0].AxisY.Minimum = 0; chart1.ChartAreas[0].AxisY.Maximum = 1; chart1.ChartAreas[0].AxisY.Title = "MCA(Total Weight Score)"; chart1.ChartAreas[0].AxisX.Interval = 1; chart1.ChartAreas[0].AxisX.Title = "Scenarios"; chart1.ChartAreas[0].AxisY.LabelAutoFitStyle = LabelAutoFitStyles.None; chart1.ChartAreas[0].AxisY.TitleFont = new System.Drawing.Font("Trebuchet MS", 8, System.Drawing.FontStyle.Bold); for (int i = 0; i < seriesArray.Length; i++) string text = ""; if (i == 0) string s = newPath.Replace("X", "Te"); text = System.IO.File.ReadAllText(s); } elseif (i == 1) string s = newPath.Replace("X", "En"); text = System.IO.File.ReadAllText(s); elseif (i == 2) string s = newPath.Replace("X", "Ec");
140
text = System.IO.File.ReadAllText(s); elseif (i == 3) string s = newPath.Replace("X", "So"); text = System.IO.File.ReadAllText(s); elseif (i == 4) string s = newPath.Replace("X", "Pr"); text = System.IO.File.ReadAllText(s); string[] dat = text.Split(';'); for (int ii = 0; ii < len; ii++) pointsArray[ii] = Convert.ToDouble(dat[ii]); Series series = this.chart1.Series.Add(seriesArray[i]); chart1.Series[i].Color = colors[i]; for (int j = 0; j < pointsArray.Length; j++) series.Points.AddXY(monthArray[j], pointsArray[j]); if (x == 0) series.ChartType = SeriesChartType.Line; series.BorderWidth = 3; privatevoid MJ11Gen4_Load(object sender, EventArgs e) dataGridView1.Rows[0].HeaderCell.Value =
"Technical";
dataGridView1.Rows[1].HeaderCell.Value = "Environmental"; dataGridView1.Rows[2].HeaderCell.Value = "Economical";
141
dataGridView1.Rows[3].HeaderCell.Value = "Social & Community"; dataGridView1.Rows[4].HeaderCell.Value = "Total Weight Score";
(M)Printing Process [System.Runtime.InteropServices.DllImport("gdi32.dll" )] publicstaticexternlong BitBlt(IntPtr hdcDest, int nXDest, int nYDest, int nWidth, int nHeight, IntPtr hdcSrc, int nXSrc, int nYSrc, int dwRop); privateBitmap memoryImage; privatevoid CaptureScreen() Graphics mygraphics = this.CreateGraphics(); Size s = this.Size; memoryImage = newBitmap(s.Width, s.Height, mygraphics); Graphics memoryGraphics = Graphics.FromImage(memoryImage); IntPtr dc1 = mygraphics.GetHdc(); IntPtr dc2 = memoryGraphics.GetHdc(); BitBlt(dc2, 0, 0, this.ClientRectangle.Width, this.ClientRectangle.Height, dc1, 0, 0, 13369376); mygraphics.ReleaseHdc(dc1); memoryGraphics.ReleaseHdc(dc2); privatevoid button1_Click_2(object sender, EventArgs e) CaptureScreen(); printDocument1.Print();
142
