Lake Tanganyika is the deepest and largest of the three East African Great Rift Valley lakes. Its ... 1.2 Reasons for Monitoring Water of Lake Tanganyika Basin .
A result report of two projects under the framework of Memorandum of Understanding between the Ministry of Science and Technology of the People’s Republic of China (MOST) and the United Nations Environment Programme (UNEP), executed by the China Science and Technology Exchange Centre (CSTEC) and the Regional Office for Africa (ROA)/UNEP.
Demonstration Research on Comprehensive Water Quality Monitoring in the Lake Tanganyika Basin Edited by Sophia S Chen and Ismael Kimirei, 2015
Performing Organization: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences African cooperation countries: Burundi, Tanzania Start-stop time: 2010/10/5-2013/12/5
Demonstration Research on Comprehensive Water Quality Monitoring in the Lake Tanganyika Basin Result Report of Two Joint Projects: Enhance the Capacity of Monitoring Shared Water Resources of Lake Tanganyika and Water Quality and Ecosystem Monitoring and Demonstration of New Waste Water Treatment Technologies
Edited by
Sophia S Chen Nanjing Institute of Geography and Limnology, Academy of Sciences, China
and Ismael Kimirei Tanzania Fisheries Research Institute, Tanzania
Published by
China Science and Technology Exchange Centre Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences 2015.1 Enquiries about this publication, or requests for copies should be addressed to: Coordinator, Coordinator, China Science and Technology Nanjing Institute of Geography Exchange Centre and Limnology, CAS 54, Sanlihe Road, Beijing 100045, 73, E Beijing Road, Nanjing China 210008, China
Preface The issue of water resources and eco-environment has been one of the major issues that seriously affect the sustainable development all over the world. China and Africa face a lot of common challenges such as the deterioration of environment, food safety problems, energy crisis, and public health threats. As a major developing economy, China has proactively promoted south-south cooperation in science and technology to tackle climate change and improve the living environment. In 2008, the Ministry of Science and Technology (MOST) of China and the United Nations Environment Programme (UNEP) signed a Memorandum of Understanding (MOU) on Framework of Technical and Institutional Cooperation on Environment in Africa, kick-starting China-Africa cooperation projects on the environment. Then in 2011 the MOU was renewed to initiate the Phase-II cooperation projects. Under this framework, Nanjing Institute of Geography and Limnology（NGLAS）has been entrusted to undertake two projects in the Lake Tanganyika Basin, involving water resources protection through capacity building, research, plan and demonstration of long-term water monitoring at the catchment scale. Lake Tanganyika is the deepest and largest of the three East African Great Rift Valley lakes. Its volume of 19,000 km3 is seven times that of Lake Victoria. Due to its rich biodiversity and fragile habitats the Lake has attracted scientists from across the world to conduct research programmes here. The NGLAS projects have complemented and enhanced the previous research, for they not only reveal that a long-term monitoring scheme on Lake Tanganyika helps to maintain a healthy lake ecosystem, but also provide affordable, low-maintenance, user-friendly means to do so in a less developed region. Through the projects the Chinese team shares their experiences and technologies in water monitoring and long-term monitoring network management; and a demonstration laboratory has been established with two sets of equipment donated by NGLAS to make long-term monitoring feasible at Kigoma, Tanzania. Besides, two training classes and several in-job training sessions have been conducted either in China or Tanzania in the past 6 years. Given the notable results, the projects have received high recognition of African partners as a paradigm of trilateral cooperation. Through the 2 NGLAS projects and other projects we carried out elsewhere in Africa, we believe south-south cooperation in science and technology holds promising prospects. While a large group of Chinese research institutions are ready to share their technologies and skills, the Chinese government will continue to strengthen science and technology collaboration with African partners to achieve common development and prosperity.
CHEN Linhao Deputy Director-General for International Cooperation Ministry of Science and Technology of China
Principal contributors: Dr. Ismael Kimirei– Coordinator in TAFIRI, Tanzania, responsible for field work in the Lake and English editing of the report Mr. Huruma Mgana – Scientist at TAFIRI Kigoma Center, responsible for the river-lake water quality monitoring in Kigoma Mr. Gabriel Hakizimana – Coordinator in Lake Tanganyika Authority and Burundi
Dr. S. Sophia Chen – Project principal investigator in Nanjing Institute of Geography and Limnology, CAS, responsible for writing sections 1, 2, 3, 6, parts of 5.2, 5.4 and editing of the report Mr. X. Chen – Coordinator in CSECT, China Mr. B.Q. Xin – Consultant in CSECT, China Dr. L. Zhang – Consultant in NIGLAS, China, responsible for writing sections 5.3 and part of 5.2 of the report Dr. Q. Gao - Consultant in NIGLAS, China Ms. C. Yu – PhD student in NIGLAS, China, responsible for writing sections 4.2, 5.5 and parts of 5.4 of the report Dr. S. H. Zhao – Consultant in Nanjing University, China, responsible for writing sections 4.1, 5.1 of the report - and his team
Contents 1 Background by SS Chen ........................................................................................................ 1 1.1 Overview of Lake Tanganyika and Its Basin ......................................................................... 1 1.2 Reasons for Monitoring Water of Lake Tanganyika Basin ..................................................... 3 2 About the Project by SS Chen .............................................................................................. 6 2.1 Initiation of the project ........................................................................................................ 6 2.2 Contents and Scheme ........................................................................................................... 7 3 Executive Summary by SS Chen ...................................................................................... 9 3.1 Cooperation Agreement ....................................................................................................... 9 3.2 Capacity Building Activities .................................................................................................. 9 3.3 Planning for a Long-term Monitoring of Water Quality ...................................................... 12 3.4 Database Construction ....................................................................................................... 14 4 Methodologies by SH Zhao & C Yu .............................................................................. 16 4.1 Method for Remote Sensing Investigation on Land Cover/use in the Basin ........................ 16 4.1.1 Land Cover/use Classification System ............................................................................. 16 4.1.2 Remote Sensing Data and Data Preprocessing ................................................................... 19 4.1.3 Methods for Interpretation of Land Cover/use .................................................................. 21 4.1.4 Change Analysis of Land Cover/use Classification............................................................ 24 4.1.5 Classification Results and Accuracy Assessments................................................................ 25 4.2 River-Lake Water Sampling and Analysis Methods .............................................................. 27 4.2.1 Water Sampling and Preprocessing Methods ...................................................................... 27 4.2.2 In-situ Monitoring Methods ........................................................................................... 27 4.2.3 Chemical Indexes and Analysis Methods .......................................................................... 28 5 Findings and Outcomes ............................................................................................................. 30 5.1 Current and Change of Land Cover/use by SH Zhao & BY Lei .......................... 30 5.1.1 Current Land Cover/use in Areas Surrounding the Lake ................................................... 30 5.1.2 Change of Land Cover/use in the Past 10 Years ............................................................... 32 5.2 Current State and Change of Water Quality by L Zhang & SS Chen ...................... 34 5.2.1 Results of investigation on the water quality of the main inflows into Lake Tanganyika ............ 34 5.2.2 Results of investigation on the water quality of northern Lake Tanganyika ............................. 41 5.2.3 Brief Summary ........................................................................................................... 44 5.3 Low-Cost Monitoring Technology Application and Effects by L Zhang .................. 45 5.3.1 Low-cost Water Quality Monitoring Technology ................................................................. 46 5.3.2 Requirements for the Application of Low-Cost Water Quality Monitoring Technology ............... 47 5.3.3 Pilot Water Quality Monitoring and Quality Control.......................................................... 52 5.3.4 Cost- Benefit Analysis of the Monitoring Technology .......................................................... 61 5.4 Plan for River-Lake Integrated Monitoring by C Yu & SS Chen ............................... 64 5.4.1 Monitoring Objectives ................................................................................................... 64 5.4.2 River-Lake Monitoring Sites Layout ............................................................................... 65 5.4.3 Monitoring Parameters and Frequency of Rivers Running into the Lake ................................. 71 5.4.4 Monitoring Parameters and Frequency in the Lake ............................................................. 71 5.4.5 Atmospheric Deposition Monitoring................................................................................. 72 5.5 Basin Data Integration and Management by C Yu & JF Gao .................................. 72
5.5.1 Database Structure and Data Source ............................................................................... 72 5.5.2 Data Management Platform .......................................................................................... 81 5.5.3 Data Use and Updating ............................................................................................... 82 6 Conclusions by SS Chen ...................................................................................................... 82 References ...................................................................................................................................... 86 Appendix: Propaganda and Achievements Issuance .................................................................. 89
List of tables Table 1 Classification system and classification standard ...................................................................18 Table 2 The wave band parameters of Landsat-5 TM data................................................................20 Table 3 Statistics of Hardware Equipment in Laboratory .................................................................48 Table 4 Comparison of single sample reagent consumption .............................................................61 Table 5 Score of monitoring effect factors and monitoring cost factors............................................62 Table 6 Lake’s basic geography data structure ...................................................................................72 Table 7 Lake’s natural attribute data structure ...................................................................................72 Table 8 Air Temperature Data Structure ...........................................................................................73 Table 9 Rainfall Data Structure .........................................................................................................74 Table 10 Evaporation Data Structure................................................................................................75 Table 11 Solar Radiation Aggregate Data Structure ..........................................................................76 Table 12 Water Environment Data Structure ....................................................................................77 Table 13 Data Storage Format ..........................................................................................................79
List of figures Figure 1 Map of the Lake Tanganyika Basin ....................................................................................... 2 Figure 2 Technical route of the project ............................................................................................... 8 Figure 3 Installing minitype weather station and atmospheric dry and wet deposition collection device in Kigoma ................................................................................................................................10 Figure 4 International training workshop ..........................................................................................10 Figure 5 Preliminary survey on water quality, soil and vegetation in Malagarasi River Basin ..............12 Figure 6 The project implementation process ...................................................................................15 Figure 7 Distribution of check points in areas surrounding Lake Tanganyika .................................25 Figure 8 Land cover in 10 km buffer area surrounding Lake Tanganyika...........................................26 Figure 9 Land cover of Lake Tanganyika Basin in 2001(a) and 2011(b) .............................................30 Figure 10 Area of various land cover types in the Lake Tanganyika Basin in 2001 and 2011 .............30 Figure 11 Change in the Area of Various Land Cover Types of Lake Tanganyika Basin in 2001-2011 .................................................................................................................................................32 Figure 12 Land cover change chart of Lake Tanganyika Basin in 2001-2011 .....................................33 Figure 13 Location of monitoring sites in 2009, 2011 by NIGLAS ................................................34 Figure 14 Location of monitoring sites (a) by CJ Ngonyani in 2002 and (b) by NIGLAS in 2012 .....35 Figure 15 Nitrogen Concentration of main stream and north tributaries of Malagarasi River ...........37 Figure 16 Phosphor Concentration of main stream and north tributaries of Malagarasi River ..........37 Figure 17 Nitrogen Concentration of Luiche River and its tributaries (2002, 2011, 2012) .................39 Figure 18 Phosphor Concentration of Luiche River and its tributaries (2011, 2012)..........................40 Figure 19 Results of investigation on water quality of northern Lake Tanganyika .............................42 Figure 20 Location of the pilot water quality monitoring sites in the Kigoma egion of Lake Tanganyika ...............................................................................................................................52 Figure 21 Mean annual value of basic environment parameters in the Kigoma region of Lake Tanganyika ...............................................................................................................................54 Figure 22 Average annual changes in nitrogen forms content in the Kigoma region of Lake Tanganyika ...............................................................................................................................57 Figure 23 Average annual changes in Phosphorus content in the Kigoma region of Lake Tanganyika .................................................................................................................................................58 Figure 24 Monitoring hardware cost .................................................................................................60 Figure 25 High-cost reagent kit .....................................................................................................60 Figure 26 Map of the water system running into Lake Tanganyika ...................................................65 Figure 27 Map of Lake Tanganyika showing the distribution of water quality monitoring sites .........69
1 Background
by SS Chen
1.1 Overview of Lake Tanganyika and Its Basin Lake Tanganyika is the deepest and largest of the three East African Great Rift Valley lakes, and the world's second deepest lake–after Lake Baikal in Siberia. It has a volume of 19,000 km3, which is equivalent to the volume of the five great lakes of North America, and seven times that of Lake Victoria. It has a maximum depth of 1470 m, and average depth of 570 m.
Lake Tanganyika is an elongate lake with a length of 670 km, and an average width of 50 km. The lake shoreline is around 1900 km in length, which is composed of rocks (43%), mixed rocks and sand (21%), sand (31%), and marsh (10%) (Coenen, et al. 1993) An important feature of the lake is its unique biodiversity; it has more than 2,000 kinds of water plants and animals, including more than 600 endemic species, which gives it an irreplaceable unique status in global biodiversity (Coulter, 1991). It has a water changing cycle (flushing time) of up 7,000 years (Coulter, 1991), so its ecological system is extremely fragile, and once polluted, Lake Tanganyika will experience an extremely slow process of natural restoration.
Lake Tanganyika is located within the western arm of the East African Rift Valley. It is harbored by four countries, named Burundi, Democratic Republic of Congo (D. R. Congo), Tanzania, and Zambia. Here, fishery resources are important sources of animal protein and the lake provides water for domestic use by the lakeside residents. Lake Tanganyika has a water surface area of around 33,000 km2 (Hutchinson, 1975), second only to Lake Victoria which has a surface area of 68,000 km2. It has a basin area of around 231,000 km2, which accommodates more than 10,000,000 people with a relatively low population density of about 43 persons/km2. Economic activities give priority to agriculture, followed by commerce and trade, and fisheries. More than 90% of the 1
population deals with agriculture. There is, to some degree, a development of light industries, such as food processing, around main cities.
Figure 1 Map of the Lake Tanganyika Basin (Compiled by JQ Zhang based on data published on line at http://www.esa-landcover-cci.org, http://www.diva-gis.org/gdata)
The main cities along the lake include Bujumbura (Burundi), Kigoma (Tanzania), Mpulungu (Zambia), Kalemi (D. R. Congo), Uvira (D. R. Congo) and some smaller towns. They hold a population of about 1,480,000 people in 2010. Kigoma is the terminal point of the Central Railway line which traverses across Tanzania mainland. Kigoma is also a shipping hub of goods and services imported into and exported from the riparian states of Lake Tanganyika, that is Burundi, D. R. Congo, and Zambia. In 2
order to accelerate the development of Central Africa, African Special Development Zone has been established here. This region cries for economic development, but the shortage of energy resources severely restricts local industrial and urban development. In recent years, this region has seen fast population growth, with the growth rate maintained at more than 3%.
1.2 Reasons for Monitoring Water of Lake Tanganyika Basin Water pollution has gradually developed from a regional problem into a global issue. Water environment monitoring technology has also developed to allow water quality monitoring systems which are mainly relying on high-performance laboratory analytical instruments and outdoor automatic monitoring devices, along with aviation and satellite remote sensing technologies. In China water monitoring aims to grasp the current state of water quality and the trend of pollution, and provide evidence for water pollution prevention, control and supervision in the basin and regional management. Water monitoring is mainly implemented through network-based organization management method, with the watershed as spatial unit and optimized river sections as basic sites, including automatic monitoring, regular monitoring, and emergency monitoring. The existing water quality monitoring technology gives priority to manual sampling and laboratory analysis, which include gravimetric method, volumetric analysis, spectrum analysis, chromatographic analysis, electrochemical analysis, and radiological analysis. The monitoring parameters include non-metal inorganic substances, heavy metals, organic substances, nutrients and microorganisms, which are mainly selected for the water bodies contaminated by inorganic and heavy metal pollutants.
Lake Tanganyika Basin has a low degree of land development, where agriculture and animal husbandry are the major land uses. Burundi’s capital, Bujumbura, is the largest city around the lake, and also regarded as the major source of pollution input into Lake Tanganyika. In addition, some fast-growing towns like Kigoma (Tanzania) are also 3
considered as potential threats to the water environment of Lake Tanganyika. In towns such as Mpulungu (Zambia), Kalemi and Uvira (D. R. Congo), mining activities have also formed certain pollution threats. In the towns around the lake, the water and soil losses caused by deforestation have obviously increased the amount of sediments entering into rivers and the lake, threatening the water environment and biodiversity in the littoral regions of Lake Tanganyika. Along with the increase of the above mentioned human activities and the global climatic change, we wonder whether the water quality of inflows through these areas will change; what are the nutritional level and distribution rule of Lake Tanganyika, and how the water & soil losses, industrial and agricultural development in the basin will change the state of the water environment. It’s difficult to answer these questions in a short term. However, we have an active demand to execute long-term and fixed-point monitoring on Lake Tanganyika, especially in the above mentioned severely affected areas.
Previous researches in Lake Tanganyika on biodiversity and habitats mostly involved water quality monitoring. Under the Lake Tanganyika Biodiversity Project (LTBP, 2000) there were specific water pollution studies, where water quality monitoring was conducted for about one year in the Burundian, Tanzanian, and Zambian parts of the lake. As part of this effort, heavy metals (Cd, Zn, Pb, Zn and Cu) distribution and nutrient pollution were also studied. The results from these studies showed that the water quality of Lake Tanganyika was generally at a healthy level (West et al., 2000), and heavy metal pollution negligible (Chale, 2000). The CLIMLAKE project, which was implemented for about 3 years, analyzed nutrients, microelements (barium and manganese), phytoplankton, and geochemical data from Kigoma (Tanzania) and Mpulungu (Zambia), and from several lake-wide cruises (Descy et al. 2000). The project also developed an ecological-hydrodynamic model to predict the upwelling and internal circulation mechanism of nutrients, and primary productivity (Plisnier, 2004; Naithani et al., 2007, 2011). Similarly, the monitoring data from this project prove that the water quality of the lake was in a healthy state. In another effort, the U. S. National Science 4
Foundation subsidized the University of Arizona to develop a research programme, The Nyanza Project, under which American and some African students were trained for six weeks annually from 1998 to 2007. Some of the students carried out water quality monitoring in the Tanzanian waters (especially Kigoma) of Lake Tanganyika. The students’ reports indicate that Lake Tanganyika water is oligotrophic, which means that the water quality is not contaminated by nutrients. Some other researches indicate that the lake’s productivity has dropped somewhat as a consequence of climate change (O’Reilly et al., 2003; Verburg et al., 2003; Tierney et al., 2010).
Furthermore, investigations in Lake Tanganyika riparian countries indicate that almost all towns along the lake lack sewage and solid wastes treatment facilities, and that these and other pollutants are generally discharged into the lake directly. There is a need to investigate how polluted lake water contributes to water-borne diseases such as cholera, exogenous febrile disease, and helopyra through water quality monitoring and analysis. Pesticide and fertilizer are another pollution source for Lake Tanganyika, despite the fact that the use of these chemicals in the catchment area is still minimal (Foxall et al., 2000)1. West et al. (2000) carried out monitoring in this aspect at rivers Lufubu (Zambia), Malagarasi (Tanzania), and Ruzizi (Burundi) and found that pesticides were also negligible, however Foxall et al. (2000) suggests a carefully designed monitoring programme so that to detect any pesticide pollution in the lake. The ships and fishery activities at ports and fishermen’s wharfs can potentially pollute the lake and cause deterioration of the water quality of the lake; it is necessary to monitor the discharges of gasoline and lubricating oil in the lake.
In recent years, sediments entering the lake have increased gradually, especially during the rainy season. This has already caused many ports to become shallow. Sedimentation also may destroy the lake habitat and pollute the water quality of areas such as the Kigoma Bay, Uvira, Bujumbura, and Kalemi. This problem is related with the non-sustainable land use activities in the basin. The special research on rivers in Tanzania 1
I. Kimirei has also mentioned the fact in a work report. 5
and Zambia has reported a preliminary conclusion that, the quantity of sediment discharged from every river to Lake Tanganyika is the function of its unit water quantity (Sichingabula, 1999; Nkotagu and Mbwambo, 2000). However, the quantity of sediments annually entering the lake can hardly been estimated for lack of survey data.
2 About the Project
by SS Chen
2.1 Initiation of the project In 2011 the Ministry of Science and Technology (MOST) of the People’s Republic of China (P. R. China) and the United Nations Environment Programme (UNEP) launched 6 projects as Phase II cooperation programme in Africa under the framework of the Memorandum of Understanding between MOSTand UNEP. The 6 joint projects intended to tackle sustainable use of water resources, water environment protection, drought early warning system, water-saving agriculture, and combating desertification. These projects focused on the Nile River, Lake Tanganyika, and the desert area in the Sahara. China Science and Technology Exchange Centre (CSTEC) was entrusted with the overall management and coordination of the implementation of the programme; while six (6) other Chinese academic institutions were designated as lead implementing agencies for the 6 projects respectively. Among the 6 projects, Project 1 and Project 3 were directly related to Lake Tanganyika. The Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS), as the state institute unique for lake science, was entrusted with undertaking the project of Phase I:
Enhance the
Capacity of Monitoring Shared Water Resources of Lake Tanganyika. In the Phase II programme, NIGLAS is the lead implementing agency for Project 3 - Water quality and ecosystem monitoring and demonstration of new waste water treatment technologies. The report with the title of “Demonstration Research on Comprehensive Water Quality Monitoring in the Lake Tanganyika Basin” presents the special study in the Lake Tanganyika Basin which is supported by Project 3.
6
2.2 Contents and Scheme The special study aims at investigation on the water environment problems of the Lake Tanganyika basin so as to develop long-term water environment and land ecosystem monitoring networks. The main contents include: (1) researching on the low-cost technology of water sampling and parameter analyses; (2) capability building of water environmental monitoring laboratories; (3) development of a comprehensive plan for river-lake water monitoring in selected areas; (4) design of a data management system for the water-based environment monitoring data; (5) demonstration of the long-term water monitoring in selected areas; and (6) in-depth training of water environment monitoring personnel. Through the project, some strategic thoughts for constructing the ability to monitor water environment will be put forward, and a water environment monitoring network and management system suitable for African countries is to be presented and demonstrated in selected basin.
Technical Route: The project is using surveying/observations, exchange visits, training, and pilot engineering to study and develop adapted technologies for ecological conservation. We use the following process to implement the project: investigation on the basin’s current water environment and ecological pressure, forming the water environment monitoring network and scheme, and demonstration of water environment monitoring in the basin. The demonstration of water environment monitoring includes raising the ability for lake environment monitoring (including laboratory construction, equipment donation, and personnel training), making and implementing monitoring scheme; establishing and sharing the water environment database management system by integrating historical materials, onsite investigation materials, and water environment monitoring data obtained through pilot monitoring.
7
Figure 2
Technical route of the project
8
3 Executive Summary
by SS Chen
3.1 Cooperation Agreement In October 2011, representatives of the Lake Tanganyika Authority were invited to NIGLAS, in Nanjing, P. R. China, to discuss the contents of the Memorandum of Cooperation with the representatives of each participating unit in the project group and the Chinese project coordination unit. After the discussion, a Memorandum of Cooperation was signed between NIGLAS and LTA—representing the four riparian countries—laying a foundation for a smooth implementation of the project in terms of cooperation relationship and mechanism.
3.2 Capacity Building Activities Provide training on instrument operation in the labs of Burundi and Tanzania In October 2011, 5 research personnel from NIGLAS visited the National Institute for the Environment and Protection of Nature (INECN) of Burundi and the Kigoma Centre of the Tanzania Fisheries Research Institute (TAFIRI-Kigoma). The team also installed the instruments and equipment donated by the project and trained laboratory personnel. The instruments and equipment mainly include spectrophotometer, balance (0.001g), portable pH meter, portable conductivity meter, low temperature drying oven, small antiseptic pot, water-bath pot, electric stove, inverted microscope, binocular microscope, computer, and other consumables.
Provide training on water quality monitoring method in Tanzania In April 2012, two researchers from NIGLAS visited the Kigoma Center of TAFIRI, and provided a ten-day hands-on training on water quality monitoring method to six (6) research personnel and laboratory technicians. 9
Installation and debugging of field monitoring equipment at TAFIRI-Kigoma In June 2012, NIGLAS donated some automatic field monitoring equipment to TAFIRI-Kigoma, and installed the equipment for them, enhancing its ability to monitor related hydrological and meteorological data.
Figure 3 Installing minitype weather station and atmospheric dry and wet deposition collection device in Kigoma
Figure 4 International training workshop 10
International training workshop on water environment monitoring and ecological protection of lake basins On September 10-25, 2012, an activity was held at NIGLAS in Nanjing, P. R. China, where a total of 15 persons from 10 African countries participated in a training workshop. The course content included: Water monitoring technology and demonstration, 3S technology in water monitoring and lake-basin management, Land cover/use change and urbanization impacts on water quality and lake ecosystems, and global climate change and ecosystem management.
Examine equipment and control quality of sample and data analysis In January 2013, a team of researchers from NIGLAS visited TAFIRI-Kigoma to examine and repair the equipment which was donated; to monitor and control the quality of the samples processing and data analysis for water quality monitoring. This was necessary because the analysis method introduced was new for the lab and needed a keen monitoring to ensure mastery of the technology.
Donate large equipment, provide in-depth training, and unveil a nameplate/plaque of a demonstration laboratory at TAFIRI-Kigoma In August 2013, NIGLAS donated an atomic absorption spectrophotometer (AAS) as one of the largest equipment donated to TAFIRI-Kigoma Center. The in-depth training on the use and how to do sample analysis using the instrument were also provided to the Center’s personnel. During the same visit, the Vice president of the Chinese Academy of Sciences (CAS), Prof. Zhang Yaping, unveiled the plaque of the Demonstration Laboratory for the East African Great Lakes Water Resource and Ecosystem Protection which is located at TAFIRI in Kigoma.
11
Quality control and test of monitoring method and data In March 2014, the NIGLAS team mainly tested the quality of the TAFIRI laboratory’s monitoring data. Meanwhile, an on-spot training was provided to the INECN laboratory personnel in Burundi.
3.3 Planning for a Long-term Monitoring of Water Quality Survey for monitoring method test In October 2011, we surveyed the upstream water quality of the Malagarasi River—a main river flowing into the lake from Tanzania—determined the sampling method and procedure, and cooperation method between Chinese and local experts.
Survey for sampling site selection
Figure 5 Preliminary survey on water quality, soil and vegetation in Malagarasi River Basin
In June 2012, five (5) persons from NIGLAS went to Tanzania to survey the northern area of the Malagarasi River Basin. The survey distance was more than 2,000 km, and the 12
results were helpful in guiding monitoring site selection and the making of the basin’s comprehensive management policies.
Consult on African urbanization and water resource On May 22, 2013, for the research on African urbanization, water resources and ecosystem protection, we invited Katherine Venton Gough—an expert researcher on African urban cities from Loughborough University, England, as well as Chinese local experts, to hold an exchange and consulting conference in Nanjing, China, which discussed related research contents, and consulted the experts on promoting the international cooperation project that NIGLAS was executing; and the follow-up research.
Develop a long-term monitoring demonstration From August 2012, a 2-year demonstration monitoring program was designed on the water quality of Lake Tanganyika and some rivers (Malagarasi and Luiche). TAFIRI-Kigoma was entrusted to develop and execute it. The program took into account both spatial and temporal changes in the lake’s water quality. The water quality monitoring was conducted once every month at two offshore sites (Malagarasi and Kigoma Bay) and three onshore/nearshore sites (Kigoma Bay, Malagarasi River mouth, and Luiche River mouth). For the offshore sites, water quality parameters (nutrients, water temperature, dissolved oxygen, conductivity, pH, and chlorophyll a) were measured at a 20 m interval to 100 m depth; while for the onshore sites sampling was conducted only to 20 m.
13
3.4 Database Construction A preliminary spatial database of the researched areas was established firstly, including land use data of Lake Tanganyika Basin, the water system, road and railway, and socio-economic data of the countries in Lake Tanganyika Basin.
By sorting out reference data, collecting historical data, collecting and producing demonstration station data, we have gathered a large dataset about the hydrology, meteorology, land use, and water quality of the Lake Tanganyika Basin. By constructing special website (http://www.protect-lake-tanganyika.cn/eindex.aspx), we have formed a network platform covering project progress, achievements, and data sharing.
14
October 2011, Bujumbura of Burundi and Kigoma of Tanzania: Field survey and onsite personnel training; October 2011, Nanjing of China: Signing the Memo of Cooperation; April 2012, Kigoma of Tanzania: Training on water quality monitoring; June 2012, Kigoma-Mwanza of Tanzania: Preliminary survey on water quality – soil –vegetation in the Tanzanian part of the Lake Tanganyika Basin; June 2012, Kigoma of Tanzania: Donating and installing instruments at the TAFIRI Kigoma Demonstration Laboratory; September 2012, Nanjing of China: Holding the workshop of international research and studies on water environment monitoring and ecological protection of the lake basin; January 2013, Bujumbura of Burundi and Kigoma of Tanzania: Attend meetings, examine and repair equipment, and provide training; May 2013, Nanjing of China: Holding a Conference of Exchange and Consulting on Research of African Urbanization and Water Resources; August 2013, Kigoma of Tanzania: Donate large equipment, provide in-depth training, and unveiled a nameplate of demonstration laboratory at TAFIRI; February 2014, Bujumbura of Burundi and Kigoma of Tanzania: Testing the quality of TAFIRI Laboratory’s monitoring data, and retraining INECN Laboratory’s personnel; July 2014, Bujumbura of Burundi and Kigoma of Tanzania: Equipment maintenance and follow-up on personnel training for TAFIR and INECN Laboratories.
Figure 6 The project implementation process
15
4 Methodologies
by SH Zhao & C Yu
4.1 Method for Remote Sensing Investigation on Land Cover/use in the Basin 4.1.1 Land Cover/use Classification System Land cover is a (biological) physical covering available to observe on the surface of the earth (Antonio and Louisa, 2005). Land cover is not only a simple land or vegetative cover type any longer; instead, it is the synthesis of land type and its series of natural attributes and characteristics (Gong et al., 2006). The change of land cover and land use is the main decisive factor affecting ecological system, global biochemistry, climatic change, and human vulnerability (Verburg et al., 2009). Along with the highlighting of global environment problems, the monitoring of land cover has attracted increasingly much attention. The Food and Agriculture Organization of the United Nations (FAO) has established a Global Land Cover Network (GLCN), in order to promote the information exchange on global land cover trend, standardized land cover charting, and global, national and regional land cover monitoring. EU has made use of independent global VGT and MERIS data to make global land cover products (GLC2000, Globcover), and realized dynamic updating for millennium environment assessment. The 1km land cover product established with the International Geosphere-Biosphere Program, Data and Information Systems (IGBP-DIS) analyzes the mechanism of change in global land cover/use, and simulates the trend of change in the future 50-100 years (IGBP, 1990).
The area studied under the project is located around Lake Tanganyika, which is shared by four African counties (Burundi, D. R. Congo, Tanzania, and Zambia). For a low population, low land development strength, and non-standard land management, land cover here is featured by great differences in the degree of crushing and poor continuity of landscapes; in lake riparian area, there are many mountainous lands and few flat lands—the landform rises and falls greatly. The climate is featured by alternation of 16
wetting and drying and non-distinctive four seasons. While the spatial heterogeneity of vertical and horizontal landscapes of land cover is strengthened; the interference by human activities and the stress of natural conditions have made the differentiation of the present spatial land cover change difficult. Surrounding Lake Tanganyika, there is a small population; the vegetation has a high coverage rate and high degree of crushing, and is mostly evergreen broad-leaf forest; wherein, artificially planted economic crops such as banana and palm trees account for a big proportion, but they are mostly distributed in areas where human activities are frequent. The farmland is mainly distributed in areas with frequent human activities along the roads and rivers. In the south, farmlands are distributed in flat areas; and in the north, they are distributed in gentle slope areas with relatively small slope and low elevation, and the flat areas with frequent human activities. Grasslands and bush woods are mostly of natural growth, have broken space distribution, and high degree of mixing. Wetland is of simplex type, but is obviously affected by surrounding landscapes; and rivers have big flow rates in rainy season, but have water interception in dry season. There are few areas without vegetation cover and area with sparse vegetation cover. The 30 m spatial resolution of Landsat TM data adopted in the present research could balance the land characteristics of each area, reflect the characteristics and change of land cover in most regions, and avoid the mixed image elements formed in crushed land plots from affecting the precision.
Combining with the characteristics of land cover in Lake Tanganyika region, and referring to the FAO/LCCS land cover/use classification system, the research defines the land cover/use classification system, mainly including six types such as forestland, grassland, wetland, farmland, artificial surface, and others. The classification system and standards are as shown in Table 1.
17
Table 1 Classification system and classification standard
No.
Classification
Forest
1
Forest land
Shrub
Garden plot
Green space
2
Grassland
Code
Types included
Indexes
101
Broad-leaved evergreen forest
Natural or semi-natural vegetation, H=3-30m, C>15%, indeciduate, broad leaf
102
Broad-leaved deciduous forest
Natural or semi-natural vegetation, H=3-30m, C>15%, deciduous leaf, broad leaf
103
Evergreen coniferous forest
Natural or semi-natural vegetation, H=3-30m, C>15%, indeciduate, needle-point leaf
104
Deciduous coniferous forest
Natural or semi-natural vegetation, H=3-30m, C>15%, deciduous leaf, needle-point leaf
105
Coniferous and broad-leaved mixed forest
Natural or semi-natural vegetation, H=3-30m, C>15%, 25%15%, deciduous leaf, broad leaf
108
Evergreen coniferous shrub forest
Natural or semi-natural vegetation, H=0.3-5m, C>15%, indeciduate, needle-point leaf
109
Arbor garden
Artificial vegetation, H=3-30m, C>15%
110
Shrub garden
Artificial vegetation, H=0.3-5m, C>15%
111
Arbor green space
Artificial vegetation, around artificial surface, H=3-30m, C>15%
112
Shrub green space
Artificial vegetation, around artificial surface, H=0.3-5m, C>15%
22
Grassland
Natural or semi-natural vegetation, K=0.9-1.5, H=0.03-3m, C>15%
23
Brushwood
Natural or semi-natural vegetation, K>1.5, H=0.03-3m, C>15%
24
Herbal green space
Artificial vegetation, around artificial surface, H=0.03-3m, C>15%
18
Forest wetland
Natural or semi-natural vegetation, T>2 or wet soil, H=3-30m, C>15%
Bushwood wetland
Natural or semi-natural vegetation, T>2 or wet soil, H=0.3-5m, C>15%
33
Herbal wetland
Natural or semi-natural vegetation, T>2 or wet soil, H=0.03-3m, C>15%
34
Lakes
Natural water surface, static
35
Reservoir/pit and pond
Artificial water surface, static
36
Rivers
Natural water surface, flowing
37
Canal/ water channel
Artificial water surface, flowing
41
Paddy field
Artificial vegetation, land disturbance, aquatic crops, harvesting process
42
Dry land
Artificial vegetation, land disturbance, xerophilous crop , harvesting process
51
Domicile
Artificial hard surface, residential buildings
52
Industrial land
Artificial hard surface, production buildings
53
Traffic land
Artificial hard surface, linear characteristics
61
Sparse forest
Natural or semi-natural vegetation, H=3-30m, C=4-15%
62
Sparse shrub forest
Natural or semi-natural vegetation, H=0.3-5m, C=4-15%
63
Sparse grassland
Natural or semi-natural vegetation, H=0.03-3m, C=4-15%
65
Exposed rock
Natural and hard surface
66
Exposed soil
Natural and loose surface, loamy texture
31
32
3
4
5
6
Wetland
Farmland
Artificial surface
Others
4.1.2 Remote Sensing Data and Data Preprocessing The Landsat-5 TM multispectral data are used in the project. The Landsat-5 TM data were sourced from the free data publicized by the United States Geological Survey (USGS, http://glovis.usgs.gov/). The scope of the researched area surrounding Lake 19
Tanganyika may be totally covered with 9 scenes of the Landsat-5 TM data. The images acquired in winter and summer are used in classification process. Wherein, considering that surrounding Lake Tanganyika, there are mostly mountainous areas and plentiful cloud pollutions, so the image of every period is spliced with the images of similar months in different years. TM5 single scene image has a coverage area of 185km×185km, the spatial resolution is 30 m, and there are 7 spectral bands in total. The parameters of wave bands are as shown in Table 2. Table 2
The wave band parameters of Landsat-5 TM data
Wave band No.
Wave band
B1
Frequency spectrum scope Median Spatial resolution (μm)
(um)
(m)
Blue
0.45—0.52
0.485
30
B2
Green
0.52—0.60
0.560
30
B3
Red
0.63—0.69
0.660
30
B4
Near IR
0.76—0.90
0.830
30
B5
SW IR
1.55—1.75
1.650
30
B6
LW IR
10.40—12.5
11.450
120
B7
SW IR
2.08—2.35
2.215
30
In order to correctly analyze the remote sensing data obtained from the satellite, it’s necessary to establish corresponding relationship between the observed quantity obtained from satellite load and the geophysical quantity observed on the ground. The objective of radiation calibration is just to establish the numerical relationship between the digital signal (DN) output by every detecting element of sensor and the radiance value input by the detector. There are generally two methods for radiation calibration: The first is to calculate radiance or reflectivity with formula; and the second is to obtain radiance or reflectivity by means of calibration with the ENVI’s intrinsic TM calibration tool. The present research adopts the first method.
The formula for calculating superficial radiance:
radiance ((lmax lmin ) /(Qcalmax Qcalmin ) (Qcal Qcalmin ) lmin 20
Where: radiance indicates apparent radiance, Qcal indicates imaging data itself (DN), lmax ,lmin means inquiry from parameters, Qcalmax is the maximum DN value; in case TM is 8bit, Qcalmax =255, Qcalmin is the minimum DN value, which is generally 0.
After radiation calibration, we carried out atmospheric correction to eliminate the difference in the radiation value of invariant objects. Finally, we cut out and inlaid the images which had been corrected. Along Lake Tanganyika, we made a 10 km buffer area to obtain the vector boundary of the study area, and to cut the images. Image inlaying is to splice the 9 scenes of the TM images cut out into a complete image of the researched area.
4.1.3 Methods for Interpretation of Land Cover/use The interpretation of Landsat-5 TM images is mainly to establish classification rule sets by adopting eCognition software. Firstly, we established the project documents with TM, slope, digital elevation map (DEM) and European Space Agency’s 300 m land cover map. Secondly, as the most important step of image classification, we executed image segmentation, and established segmentation layer. Lake Tanganyika covers a big area, owns relatively uniform texture, and has very small rivers in surrounding areas. It is obviously affected by surrounding non-water bodies, especially vegetation. We segmented the images twice, and extracted the lake and surrounding very small rivers respectively.
Firstly, we distinguished water bodies and branch water bodies. The spectral signature of water is mainly determined by the intrinsic material compositions of water, and meanwhile, affected by various states of water. Considering the characteristics of water body, we selected the composite function of green band and near infrared band (NDWI, normalized water index) to distinguish water bodies and non-water bodies. 21
NDWI
green nir green nir
Where, green indicates the reflectivity of water body at green optical band, and
nir indicates the reflectivity of water body at near infrared band. By adjusting the threshold value of NDWI, we may well distinguish water bodies and non-water bodies. In order to extract very small rivers, we adjusted the segmentation scale. We established a new segmentation layer, and adjusted the segmentation scale to 10; the images participating in segmentation are TM, digital elevation map and slope map. Rivers have obvious shape characteristics and the characteristics of water body, so we extracted rivers by means of combination classification function. The shape index formula is expressed as:
R
A P
Where, A indicates the object’s area, and P indicates the object’s perimeter. It’s available to realize the extraction of rivers by combining with NDWI and shape index.
Secondly, we extracted artificial surface. Considering the sparse artificial surfaces which are heavily affected by surrounding vegetation on the image, we selected appropriate segmentation scale. Firstly, relatively large cities were extracted according to their location characteristics and intrinsic spectral signature. The cities are distributed along the lake and roads, so the European Space Agency’s special classification maps are taken into segmentation based on the scale of 25. By analyzing the characteristics of artificial surfaces, we discover that, between TM4 and TM5 bands, the DN value of surface features becomes smaller, except that of urban lands becomes bigger. By establishing NDBI = (TM5-TM4)/(TM5+TM4), we determined that the surface features with NDBI value of bigger than 0 on the image can be considered as urban lands. However, in Landsat TM image, the surface features with DN value of TM5 higher than that of TM4 are not only urban lands, but also bare lands, and low-density vegetation 22
cover areas containing soil background information. Therefore, the precision of urban land extraction only with NDBI will surely be affected at a certain degree. Normalized vegetation index (NDVI) reflects vegetation information, so 1-NDVI reflects non-vegetation information, namely settlement place, bare land and rivers. NDBI mainly reflects urban and bare land information, so the combination of NDBI and 1-NDVI will highlight settlement place information still better. MNDBI=NDBI+(1-NDVI). Improved NDBI could strengthen the images; the scope of change in grey level is bigger than the change of NDBI, and this highlights urban information, enlarges its difference from surrounding surface features, and is beneficial for extracting urban information.
Thirdly, roads and rivers have the same shape characteristics, so we extracted road information by combining with shape index. For extraction of small-density artificial surfaces, we determined the appropriate segmentation scale and extraction method. We then established new segmentation layer, and adjusted multi-scale segmentation parameters. The images participating in segmentation include TM5, slope and digital elevation map. Small towns are distributed along roads, and sparse towns are extracted by combining with MNDBI index.
Finally, we extracted vegetation information, which was done by calculating the normalized vegetation index using the formula:
NDVI
nir red nir red
Where nir indicates the reflectivity of surface features at near infrared band, and
red indicates the reflectivity of surface features at red band. The vegetation types in the researched area mainly included forest land, grassland, bush wood and farmland, and the mixture of various vegetations. For distinguishing vegetation types, we determined the appropriate segmentation scale and distinguishing indexes. Forest land and other vegetation types are easy to distinguish. While forest land included palm trees and banana forests, we considered NDVI, texture characteristics and geographical position 23
characteristics to distinguish forest land and other vegetations. Palm and banana forests, as economic forest, are mainly distributed around settlement places and the areas along the roads; so we need to consider the characteristics of geographical position. Farmland has characteristics similar to those of economic forests, and its geographical position has obvious characteristics. The main crops include cassava, peanut, paddy, and potato, but the spectral signature is similar to that of grassland and bush wood. Therefore, grassland, bush wood and crops are mainly distinguished by the slope and texture characteristics. Bush wood and grassland are mostly mixed which make them, difficult to distinguish. According to field survey, bush wood is mostly distributed on mountain slopes, while grass is mostly distributed at the foot of mountains, and the location characteristics are obvious.
4.1.4 Change Analysis of Land Cover/use Classification We have disposed and analyzed the change in land cover/use classification aiming at the Landsat TM images of the researched area in two periods—2001 and 2011—mainly including the two aspects, namely change information identification and change information extraction. The detailed steps are as shown below: (1) Analysis on alternative display of images: The images of two periods are displayed simultaneously on the screen: (2) Analysis on color integration of different time-phase bands: Display the combined images of near infrared, red, and green bands of 2 different time phases, and determine variant information and non-variant information. (3) Analysis on the images generated through reprocessing after synthesis of two times phases: Use rule set classification method and supervision classification method to generate multi-band data of two time phases into images inside new layer. (4) Image algebra processing: Carry out per-pixel subtraction of the images in the two periods. Analyze the histogram information of operation result, and determine the standard deviation and threshold value for distinguishing variant information and 24
background information. (5) Change Vector Analysis (CVA): used to determine the changes of types in 2001-2010: Target surface features are expanded in area, target surface features are decreased in area, and target surface features are not changed.
4.1.5 Classification Results and Accuracy Assessments Through classification by adopting multi-source data and combining with the rules of eCognition software, we can obtain the information on land covers within 10 km of Lake Tanganyika’s buffer area. The land covers are classified into six types: forest land, grassland, water body, artificial surface, farmland and others. Forestland, grassland and farmland account for the largest proportion; while artificial surface accounts for a small proportion. According to the area of land covers, we have selected 620 check points (Figure 7) to examine the land cover classification precision (Figure 8). While, 550 check points are correct, the other 70 check points have either wrong classification or misjudged as being a mixed type. The overall precision is 88.7%. Wherein, in the interpretation result of artificial surface, in some areas, artificial surface is omitted in classification for sparse artificial surfaces and the influence of surrounding vegetations.
Figure 7 Distribution of check points in areas surrounding Lake Tanganyika 25
Figure 8 Land cover in 10 km buffer area surrounding Lake Tanganyika
26
4.2 River-Lake Water Sampling and Analysis Methods 4.2.1 Water Sampling and Preprocessing Methods River water sample collection and storage methods Two 500 ml sampling bottles were used to collect water samples from the surface layer of river water. The sampling bottles were put against the direction of water flow, while preventing floating substances from entering the bottles. One water sample (500 ml) was filtered through a 0.45 um filter membrane into two 15 ml centrifuge tubes, to which 0.2 ml zinc chloride was added to fix the water sample; the fixed samples were then used to determine dissolved inorganic pollutants in the water. Then two bottles of 100 ml were collected and preserved for the determination of alkalinity, COD, total nitrogen, total phosphorus. The second bottle of 500 ml water sample was used for the determination of total suspended solids (TSS) concentration in the laboratory. All water sample collected were stored at 4°C in the refrigerator.
Lake water sample collection and storage methods Water samples were collected using the organic glass water sampler. The sampler operates such that the water flows through it during the descending movement, closes at the intended depth when the bottle stops moving and prevents water from entering the bottle when it ascends to the surface. The lake water samples were collected at an interval of 20 m from the surface (0 m) to 20 m for the nearshore water areas; while for the offshore sites, water samples were collected at 0m, 20m, 40m, 60m, 80m and 100m, which constituted six layers. The pretreatment and preservation methods of water samples are the same as that of water sample from rivers.
4.2.2 In-situ Monitoring Methods The YSI multi-parameter water analyzer was used to monitor the physical and chemical water quality parameters, such as temperature, dissolved oxygen (DO), pH, electric 27
conductivity (EC), oxidation reduction potential, and salinity; while a 20 cm white-and-black painted round disc (Secchi disc) was used to measure water transparency in meters.
4.2.3 Chemical Indexes and Analysis Methods The chemical water quality parameters of rivers mainly included: concentration of total suspended solids, nitrate nitrogen, nitrite nitrogen, ammonia nitrogen, total nitrogen, soluble reactive phosphorus and total phosphorus.
The total suspended solids concentration of the river water samples was measured after allowing the samples to sediment at 4℃ in the refrigerator for two days. A pre-weighed membrane filter (0.47um) was first oven-dried at 104℃ for 1h, and then used for the determination of TSS. Depending on the turbidity of the water, 500 ml of well and sufficiently mixed water sample was measured and suction-filtered through the pre-weighed oven-dried 0.47μm membrane filter. The quantity of water to be used for this analysis may be reduced according to actual situation especially when the water sample is highly turbid. After suction filtration, the filter membrane bearing suspended solids was then oven-dry for 1h at 104℃, after which it was allowed to cool down to room temperature. The weight of the filter with the suspended materials was then measured and the concentration of suspended solids determined from the difference in the weight of the filter before and after treatment, and the volume of water sample filtered.
Total nitrogen (TN) was measured spectrophotometrically using the Potassium persulfate oxidation method (APHA, 1998). Total phosphorus was measured with molybdenum antimony spectrophotometric method; while ammonia nitrogen, nitrate nitrogen, nitrite nitrogen and phosphate were analyzed using the continuous flow colorimetric method. 28
The chemical water quality parameters measured for lake water included: nitrate nitrogen, nitrite nitrogen, ammonia nitrogen, total nitrogen, total phosphorus, DTN, DTP, and CODMN.
Total nitrogen of lake water was measured spectrophotometrically with the Potassium
persulfate
oxidation
method.
Total
phosphorus
was
measured
spectrophotometrically using the molybdenum antimony spectrophotometric method. DTN was measured from a filtered water sample using the Potassium persulfate oxidation UV spectrophotometric method (APHA, 1998). While nitrate nitrogen was measured through UV spectrophotometry of filtered water sample, nitrite nitrogen and ammonia
nitrogen
were
measured
from
filtered
water
samples
using
the
N-(1-naphthyl)-Ethylenediamine spectrophotometric and Nessler’s reagent colorimetric methods respectively (APHA, 1998). Chemical oxygen demand (CODMN) was measured using the acid potassium-permanganate method (APHA, 1998).
29
5 Findings and Outcomes 5.1 Current and Change of Land Cover/use
by SH Zhao & BY Lei
5.1.1 Current Land Cover/use in Areas Surrounding the Lake Lake Tanganyika is a freshwater lake which is located between the East and Central Africa—within the western arm of the East African Rift Valley. It is the deepest and the second largest lake in Africa. Along the banks of Lake Tanganyika, there are crops such as paddy and palm trees, and abundant forest resources. The important ports along the lake include Bujumbura, Kalemi, Ujiji and Kigoma—both located in Kigoma. The conditions for agricultural cultivation are good, including water, climate and soil. The population is relatively concentrated in areas around towns and cities, and along roads in villages. These areas are also surrounded by a relatively high concentration of farmland resources, forming the main source of food for the population.
Figure 9 shows the land cover maps of 10 km areas surrounding Lake Tanganyika in 2001(a) and 2011(b); and Figure 10 shows the area chart of various land cover types in 2001 and 2011. The basic land cover/use types include: farmland, forestland, grassland, artificial surfaces (mainly settlement place and surrounding hardened ground, and infrastructures), wetlands (including rivers and lakes), as well as other types (sparse vegetation, exposed soil, and bare rock).
According to the proportion of various land types in Figure 9, the areas surrounding Lake Tanganyika have lower proportions of farmlands, while the proportion of water areas—wetlands– is the highest followed by the forest land. It can be deduced from Figure 10 that the wetland area of Lake Tanganyika is around 32726.8 km2, accounting for more than 50% of the researched area. The other types of land cover such as grassland are also distributed in areas around the lake but their coverage is small. It can also be observed from the figure that there is a slight increase of farmland and a decrease in forestland between 2001 and 2011. 30
(a) Figure 9
(b)
Land cover of Lake Tanganyika Basin in 2001(a) and 2011(b)
Figure 10 Area of various land cover types in the Lake Tanganyika Basin in 2001 and 2011
31
5.1.2 Change of Land Cover/use in the Past 10 Years Figure 11 shows land cover/use changes for the period 2001-2011. According to the figure, it can be observed that forest area decreased by nearly 200 km2, while the wetland area only shows a slight decrease over the same period. On the other hand however, farmland and grasslands have increased. It should be noted that Figure 11 only shows the overall change of areas surrounding the lake. Obviously, the changes are relatively small, and mainly centralized and distributed around some cities. Although the change in the proportion of farmland and forestland cover was slightly obvious—but less so in the other land use types—the overall structure of land types was relatively stable. The land cover/use change may have resulted from a combination of factors where climatic change and human activities are the most important ones. The growth of non-agricultural lands, especially where there is a fast urbanization process, could be the other factor. However, the growth of farmlands caused by intensification of agriculture and technological development and high demand for food to sustain the growing population in the region, could have caused the large decrease in forestland cover observed during the 10 years (2001-2011) that was analyzed (Figure 11).
Along with the enhancement of living standards and the improvement of, and access to medical services, population has grown more and more quickly in the region, which has caused the increase in food demand. In most African countries, agricultural technology is relatively laggard, and crop output is relatively low. In order to increase farmlands and enhance crop output, it certainly requires larger-scale forest clearance and virgin land cultivation.
The wetland area decreased by 0.05% in 10 years, which may indicate presence of some drought phenomena and intensification of cultivation on virgin land in areas surrounding the lake in 2001-2011. Urban expansion may also induce the occupation of surrounding water areas. Through comparative analysis on the classification results of the two periods indicate that the lake area is also decreasing, and the lake level is 32
following the same trend despite some seasonal filling.
Figure 11 Change in the Area of Various Land Cover Types of Lake Tanganyika Basin in 2001-2011
The increase in artificial surface, observed in Figure 11, can be linked to high population growth and the demand for better infrastructure. Most African countries have relatively slow industrialization process and limited infrastructure construction, which may have led to relatively small increase in cities (0.05%) over the 10 years period. Moreover, the grassland and other exposed soil areas increased slightly probably as a result of decreasing forestland and increasing farmlands—unsustainable agriculture is known to cause soil erosion which can be reflected as exposed soil during classification.
33
Figure 12 Land cover change chart of Lake Tanganyika Basin in 2001-2011
5.2 Current State and Change of Water Quality by L Zhang & SS Chen 5.2.1 Results of investigation on the water quality of the main inflows into Lake Tanganyika The Nanjing Institute of Geography & Limnology (NIGLAS) investigated the water quality of two main rivers running into the lake in Tanzania—Kigoma region—in October 2011 and June 2012 respectively. They are the Luiche River and the Malagarasi River. The Luiche River, which has a greatly rising and falling topography, and agriculture as the main and priority land use feature, enters the lake from northeast—relative to the lake—at Ujiji in Kigoma town. The Malagarasi River, which is the largest river running into Lake Tanganyika, has a topography that doesn’t rise and fall greatly as Luiche, and the land cover/use is dominated by natural and semi-natural forest and grassland. 34
Figure 13
Location of monitoring sites in 2009, 2011 by NIGLAS
Water quality of Malagarasi River and north branches It was discovered, according to the investigation on the water quality of rivers in Malagarasi Basin in June 2012 that:
The pH value of the water in the Malagarasi wetland ranged between 6.79 and 8.32. The pH values of trunk streams and the lake waters that trunk streams flow through were higher than the values from the north branch of the river. The highest value (8.32) was measured in Lake Nyamagoma. The relatively high pH value indicates that lake waters have relatively high primary productivity than in fast flowing river waters. For particulate sampling sites at the northern branch of the Malagarasi in areas with 35
slow-flowing waters, the pH value is also relatively high. This is probably related to industrial pollution and algae formation—which grow in relatively slow moving or stagnant water bodies. Slow-flowing water bodies have weak dilution function, so the pH value of water becomes relatively high. (a)
(b)
Figure 14
Location of monitoring sites (a) by CJ Ngonyani in 2002 and (b) by NIGLAS in 2012 36
The electric conductivity (EC) values of the water in the Malagarasi basin indicated that all water bodies in the basin have a relatively low total ion content, especially the northern fast-flowing sections, probably due to the short-term contact with the underlying soil, or the existence of mainly sand and rocky riverbed that low contents of ions dissolve into the overlying waters. In lakes and slow-flowing water bodies, the accumulation of dissolved ions in the basin has induced high electric conductivity. The highest electricity conductivity of 502 μScm-1 was measured in Lake Nyamagoma (KK1). This value is similar to that of Lake Tai and Lake Chao in the middle and lower reaches of the Chinese Yangtze River. In the Ngarangasa River (KK35) and Makere River (KK37), among north tributaries of Malagarasi, electric conductivity is even less than 60 μScm-1, showing typical headstream water body characteristics. In the south trunk streams, the average value is 268 μScm-1, which is significantly higher than 135 μScm-1 averagely recorded in the northern tributaries.
The concentration of ammonia nitrogen in the basin is not high; it ranges between 0.03 and 0.29 mg/L. While the average value of ammonia nitrogen in the north tributary waters was 0.08 mg/L that of southern main stream waters was 0.06 mg/L. The maximum value (0.29 mg/L) appeared in the Kalembela River (KK26) one of northern tributaries.
According to the Chinese surface water quality evaluation
standards, most of the monitored water bodies belong to Class-I water. The content of nitrate in the basin is similarly at a relatively low level, and is within the scope of 0.002-0.32mg/L. Especially in some north rivulet waters, the content is even lower than the method’s detection limit. The average total nitrogen is 0.6 mg/L and ranged from 0.31 to 1.39 mg/L; the differences in the content between the tributaries and the main streams failed to pass the significance test with a confidence level of 95%.
37
Main stream
Tributaries
Figure 15 Nitrogen Concentration of main stream and north tributaries of Malagarasi River (KM1, KM2 etc. are monitoring sites in 2002, 2011, 2012 as shown in Fig.13-14)
The total phosphorus values were 0.012-0.152 mg/L, with the maximum value recorded in the Muyovozi River (KK25). While the average value for the main stream was only 0.018 mg/L, that for the tributaries reached up to 0.04 mg/L.
Main stream
Tributaries
Figure 16 Phosphor Concentration of main stream and north tributaries of Malagarasi River 38
Total dissolved organic carbon ranged between 2.1 and 33.2 mg/L. Again the average value in the main streams was lower (3.94mg/L) than in the tributaries which was about three times higher (i.e. 10.01mg/L). The maximum values appeared in the Mvuvuma River (KK25). This river is located in an agricultural area indicating that agricultural activities may contribute to the organic substances of the river.
The average sulfate content was 5.5 mg/L (range: 3.8-19.5 mg/L). There was basically no difference between the main stream and tributaries.
The chloride ion content averaged at 5.8 mg/L (range: 3.6-15.8mg/L), with the maximum value appearing in the main streams. The average value for the southern main stream section was higher (i.e. 9.2mg/L) than the northern tributaries which was only 4.4 mg/L. However, the fluorine ion content was slightly higher in the main streams (0.17mg/L) and low in tributaries (0.14mg/L).
The content of the main positive ions (potassium, calcium, sodium and magnesium) in the water differed greatly, with values ranging from 9.0 to 110.5 mg/L. The north Mungonya River (KK32) had the lowest content of positive ions, while Lake Nyamagoma (KK1) had the maximum values of the same. The correlation coefficient between the content of positive ion and mineralization of water was 0.78, indicating that mineralization is controlled by positive ion.
The concentration of aluminum was relatively high in the main streams (range: 0.12 - 0.22 μg/L) and Lake Nyamagoma (KK1: 0.58μg/L); while that in the Mgandazi River (KK28) and Ngarangasa River (KK35), among north tributaries, was even as low as the detection limit.
Chromium (Cr) concentration values ranged between 0.02 and 0.36 μg/L, with the 39
maximum value being recorded at the site close to the railway bridge on the main Malagarasi River near Uvinza (KK6); but the reasons are not clear yet. The distribution pattern of copper (Cu) was similar to that of chromium, that’s to say, that sites with relatively high chromium content, also had relatively high Cu values.
Mercury (Hg) values were between 0.04 and 0.10 μg/L, but with no obvious differences in concentration values between the trunk stream and tributaries. However, unlike Hg, the content of cadmium (Cd) was characterized with higher values in the southern trunk streams (range: 0.015-0.026 μg/L) than in the northern tributaries. Among the tributaries, the cadmium content was mostly as low as the detection limit except for that in Muyovozi River (KK41: 0.084) and Kalembela River (KK26: 0.008). Likewise, the contents of lead and arsenic were higher in trunk stream (Pb = 0.01-0.3μg/L; As = 0.24-0.27μg/L) than in tributaries (Pb = 0.01-0.03 μg/L; As = 0.04-0.52 μg/L).
Luiche River Total nitrogen concentration in the Luiche River ranged between 0.21 and 0.40mg/L. These values were generally lower than the concentration value measured in the Malagarasi trunk stream and northern tributaries (Figure 17), but higher than that measured in the trunk streams in October 2011.
2012.6
Figure 17
2011.10
2002.7
Nitrogen Concentration of Luiche River and its tributaries (2002, 2011, 2012) 40
Total phosphorus (TP) concentrations were between 0.015 and 0.032 mg/L, which were generally equivalent to the values measured in the Malagarasi trunk stream, but obviously lower than TP values in the northern tributaries (Figure 18). The TP values were generally on the higher in comparison with the values measured in October 2011.
2012.6
Figure 18
2011.10
Phosphor Concentration of Luiche River and its tributaries (2011, 2012)
5.2.2 Results of investigation on the water quality of northern Lake Tanganyika The Nanjing Institute of Geography & Limnology investigated the water quality in the northern areas of Lake Tanganyika in February 2009 and October 2011 respectively. The water samples for analysis were obtained from the near-shore and off-shore areas of Bujumbura and Kigoma.
The results from this investigation indicate that, for 2011 (T01, T04, T05-1, T06 as shown in Fig.13a ), total phosphorus of Lake Tanganyika in the inshore areas was below 0.02 mg/L which, according to the Chinese surface water quality standard (GB3838-2002), belongs to Class I-II water. However, the offshore waters had TP values lower than 0.01mg/L, therefore belonging to class-I water standards. The surface waters in the Malagarasi area (i.e. T05-1) had higher TP content than deep-layer water, probably indicating the influence of the Malagarasi River—which may be influencing high 41
phytoplankton production and high sediment input, resulting in high TP values. What’s more, the TP in the lake is mainly formed by the dissolved phosphorus component, while the particulate phosphorus fraction is extremely low. The monitoring results indicate that TP values measured in the surface water layer (0-1 m) is comparable to the TP values measured in 2009 (kig1, kig2, kig3, bu1, bu2 as shown in Fig.13a) and by the LTBP project in 1998-1999 (TK1, TK3, TK4, TW, TT, TK2), where most values range between 0.01 and 0.015 mg/l (Figure 19a). However, the TP value of bu1 is the highest of all. This site is located in the Burundi port so possibly this conspicuously high value can be related to pollution from ship at the port or from urban pollution. On the contrary, TK2, which was considered as the control point and located further offshore, has relatively low TP concentrations (Figure 19a). Furthermore, the concentration of dissolved phosphate in Lake Tanganyika and other water bodies in its basin is extremely low, in some occasions it is even lower than the method’s detection limit (10μg/L) (Figure 19c). This may indicate that phosphorus is the most limiting nutrient in the lake; also that the turnover rate of this nutrient is very high in the lake (Jarvinen et al., 1999; Kimirei and Mgaya, 2007).
The total nitrogen (TN) distribution pattern is similar to that of total phosphorus. While dissolved total nitrogen prevails as the dominant component of TN in the lake, the particulate total nitrogen fraction slightly rises in areas affected by river inflows and urban pollutions. Likewise the Bujumbura port site (bu1) had slight high TN value which was about a magnitude higher than that of TK1 (Figure 19b). According to the Chinese lake and reservoir water standards, the TN values of Lake Tanganyika meet the Class I-II water standards which are lower than the content of river-ways total nitrogen standard values (GB3838-2002).
42
(a)
(b)
(c)
(d)
Figure 19 Results of investigation on water quality of northern Lake Tanganyika (The short names on x-axis refer to sampling sites on the Lake.)
43
According to the 2011 measurement result, ammonia nitrogen was found to be the main form of dissolved inorganic nitrogen both in Lake Tanganyika and river waters. Unlike the nitrate values measured in 1998-1999 under the LTBP project which is around 0.05mg/L, nitrate values measured in 2011 were mostly below 0.004mg/L (Figure 19d). Both the lake and the rivers generally have high dissolved oxygen concentrations; so the relatively high proportion of ammonia nitrogen relative to nitrate nitrogen is possibly related to the denitrification function in these waters. The concentration of ammonia nitrogen in the lake was mostly lower than 0.2 mg/L (Figure 19d).
The pH value was found to be slightly lower at the river mouth, and considerably higher (around 9) in rivers running into the lake and in the main lake, which is higher than that of the freshwater lakes in the middle and lower reaches of the Chinese Yangtze River.
5.2.3 Brief Summary The
results
of
water
quality
monitoring
in
the
main
rivers—and
their
tributaries—running into Lake Tanganyika, and northern lake areas show that Lake Tanganyika basin waters are generally characterized by low nutrient values, high alkalinity and pH values, and heavy metals with below detection limit concentrations. In spatial distribution context, TP and TN values are higher in rivers than in lakes; while concentrations in near-shore areas of the lake are higher than in offshore waters, indicating that rivers are important sources of nutrients in the lake. Nutrients concentrations in the main river and tributaries of the Malagarasi River—which is the main river flowing into the lake—generally do not vary significantly. However, TP and TN values in the northern tributary waters are by far higher than those of the main river. The northern tributaries are located in areas with intensive agricultural activities, which indicate the distinctive contributions of agriculture to organic substances into rivers. When temporal changes are considered by comparing the data obtained in 2011 and 44
2012 with studies conducted in in 1998-1999, TP does not show obvious changes in the lake areas studied. In addition, chromium and copper metals were detected in the western arm and northern tributaries of the Malagarasi River, where chromium (Cr) content was between 0.02 and 0.36μg/L; however the reason for the presence of the values have not been elucidate yet, and will need some more data and analysis.
5.3 Low-Cost Monitoring Technology Application and Effects by L Zhang Applicable low-cost monitoring is to preferentially adopt a low-cost water environment monitoring method which is feasible economically, technically and methodologically; meets local economic development level in the construction of monitoring method and system by considering the socio-economic development level, and comprehensively considering the need for long-term and sustainable water environment monitoring.
Modern methods for water environment monitoring include satellite remote sensing, and high frequency in-situ monitoring which is sometimes automatically uploading data to websites where they can be accessed online. In the traditional monitoring methods, the use of highly precise but expensive analysis equipment restricts the application and capability of water environment monitoring, especially in developing countries. All countries in Lake Tanganyika Basin are low-economy and developing countries. Therefore it is difficult for such countries to bear the costs of the expensive instruments and equipment necessary for monitoring water environment with modern methods. Furthermore, the high-tech equipment requires regular maintenance and management which is a costly investment. For example, online monitoring could obtain continuous and instant monitoring results, but a very high amount of initial input is required in equipment and maintenance of sensors. Also the online monitoring usually requires plentiful human and economic resources; therefore, it cannot be widely applied in developing countries. In-situ monitoring and onsite fast detection usually require a high 45
cost of reagents and electrode maintenance. Based on the consideration of economic factors, these monitoring methods usually cannot be applied continuously in low-economy-developing countries, so they go against the long-term demand of water environment monitoring.
5.3.1 Low-cost Water Quality Monitoring Technology Comprehensively considering monitoring effect and method, we have given up sensor monitoring, online monitoring, and high-precision instrument analysis methods, especially, we changed the reagent-kit of the fast measurement method originally adopted in many projects by countries in Lake Tanganyika Basin, thereby making the long-term water environment monitoring of the lake feasible.
Low-cost water quality monitoring parameters Physico-chemical properties: Transparency (SD), Water temperature (Tw), pH, Electric conductivity (EC), and Turbidity (Suspended particulates, SS), Nutrients: Ammonia nitrogen (NH4), Nitrate (NO3), Nitrite (NO2), Phosphate (PO4), Total nitrogen (TN), Total phosphorus (TP), Dissolved total nitrogen (DTN), Dissolved total phosphorus (DTP), and Chemical oxygen demand (CODMn) Primary productivity: chlorophyll (Chla), and primary productivity (GPP, NPP). Heavy metal in water and fishes: Copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), and other metals such as iron (Fe), and manganese (Mn), etc..
Low-cost water quality monitoring methods Methods for testing basic physical and chemical properties: a) Transparency (SD): Secchi Disk (Secchi Disk) b) Water temperature (Tw): Water thermometer c) pH: Portable pH meter d) Electric conductivity (EC): Portable electric conductivity meter 46
e) Turbidity (particulate matter, SS): gravimetric method, filtration, drying and weighing method Methods for determining the nutrients levels: f) Ammonia nitrogen (NH4): Nessler’s reagent and colorimetric methods g) Nitrate (NO3): UV colorimetric methods h) Nitrite (NO2): p-aminobenzene sulfonamide colorimetric methods i) Phosphate (PO4): molybdenum blue colorimetric methods j) Total nitrogen (TN): Potassium persulfate digestion UV colorimetric methods k) Total phosphorus (TP): Potassium persulfate digestion molybdenum blue colorimetric methods l) Total dissolved nitrogen (TDN): Filtration- Potassium persulfate digestion–UV colorimetric methods m) Total dissolved phosphorus (TDP): Filtration- Potassium persulfate digestion molybdenum blue colorimetric methods n) Chemical oxygen demand (CODMn): Acidic potassium permanganate oxidation method Method for determining the primary productivity indexes: o) Chlorophyll (Chla): Acetone extraction method p) Primary productivity (GPP, NPP): Light and dark bottle incubation (24hr) 5.3.2 Requirements for the Application of Low-Cost Water Quality Monitoring Technology Requirements on water quality analyzers Low-cost water environment monitoring method gets rid of the hardware requirement of high-precision instruments, but adopts classical water environment monitoring methods such as volumetric, spectrophotometric and colorimetric methods; weight analysis, etc.; meanwhile, such methods meet EPA and China’s surface water and waste water monitoring standards. For different monitoring parameters, the monitoring hardware and basic equipment required are as shown below: 47
Determination of basic physical and chemical properties: a) Transparency (SD): Secchi Disk (Secchi Disk) b) Water temperature (Tw): Water thermometer c) pH (pH): Portable pH meter d) Electric conductivity (EC): Portable electric conductivity meter e) Turbidity (SS): balance, low-temperature baking oven (200℃), glass-fiber filter membrane. Determination of nutrients levels : a) Ammonia nitrogen (NH4): colorimetric cylinder, spectrophotometer b) Nitrate (NO3): colorimetric cylinder, spectrophotometer c) Nitrite (NO2): colorimetric cylinder, spectrophotometer d) Phosphate(PO4): colorimetric cylinder, spectrophotometer e) Total nitrogen (TN): colorimetric cylinder, spectrophotometer, small sterilizing pot/autoclave f) Total phosphorus(TP): colorimetric cylinder, spectrophotometer, small sterilizing pot/autoclave g) Total dissolved nitrogen (TDN): colorimetric cylinder, spectrophotometer , small sterilizing pot/autoclave, glass fiber filter membrane h) Total dissolved phosphorus (TDP): colorimetric cylinder, spectrophotometer , small sterilizing pot/autoclave, glass fiber filter membrane i) Chemical oxygen demand (CODMn): burette, and water-bath kettle Methods for the primary productivity index of lakes: j) Chlorophyll (Chla): spectrophotometer k) Primary productivity (GPP, NPP): dissolved oxygen bottle, burette
In addition, for volumetric analysis, weight analysis, and colorimetric analysis, glassware are also needed, such as the beakers and volumetric flasks for preparing reagents, as well as the refrigerator necessary for storing samples.
48
The hardware equipment for monitoring the abovementioned indexes is as follows:
Table 3 Statistics of Hardware Equipment in Laboratory
Analytic instruments
Laboratory
Glassware
equipment 1
2
Spectrophotometer
Balance (0.001g)
Other hardware equipment
Low-temperature
Glass-fiber filter
Secchi Disk
baking oven
membrane
small sterilizing
Colorimetric cylinder
Water thermometer
pot/autoclave 3
Portable pH meter
Water-bath kettle
Acidic burette
Vacuum pump
4
Portable electric
Refrigerator
Basic burette
Aurilave
Electric stove
Beaker
conductivity instrument 5 6
Volumetric flask
7
Glass rod
8
Dissolved oxygen bottle
9
Suction pipette
Preparation of reagents for laboratory analysis Dissolved oxygen DO: Concentrated sulfuric acid (H2SO4) Manganese sulfate (MnSO4·4H2O) Starch (Analytical grade) Potassium dichromate (K2Cr2O7) Sodium hyposulfite (Na2S2O3·5H2O) Sodium hydroxide (NaOH) Potassium iodide (KI) 49
Nitrate NO3Hydrochloric acid (HCl) Nitrite NO2Phosphoric acid (H3PO4) ρ=1.7g/ml Sulfanilamide sulfanilic amide ((NH2C6H4SO2NH2) N-(1-naphthy)ethylenediamine dihydrochloride (C10H7NHC2H4NH22HCl) Sodium oxalate (Na2C2O4) Potassum permanganate (KMnO4 ) Sodium nitrite (NaNO2) Concentrated sulfuric acid (H2SO4) Ammonia nitrogen NH4+ Ammonia chloride (NH4Cl): Salicylic acid (C6H4(OH)COOH) Sodium hydroxide (NaOH) Sodium hypochlorite (NaHClO3) Sodium nitroprusside (Na2(Fe(CN)6NO.2H2O) Total nitrogen /total phosphorus/phosphate (TN/TP/PO43-) Sodium hydroxide (NaOH) Potassium persulphate (K2S2O8) Potassium nitrate (KNO3) Potassium dihydrogen phosphate (KH2PO4) Ammonium molybdate ((NH4)6Mo7O24·4H2O) Antimonyl potassium tartrate (KSbC4H4O7·1/2H2O) Ascorbic acid (C6H8O6) Concentrated sulfuric acid (H2SO4) Chlorophyll a (Chla) Ethanol (90% v/v) hydrochloric acid (HCl) Degree of mineralization (Salinity) 50
Hydrogen dioxide (H2O2) CODMn Sodium oxalate (Na2C2O4) –Standard Potassum permanganate (KMnO4) – titration solution Concentrated sulfuric acid (H2SO4) Alkalinity Phenolphthalein (C20H14O4) – 1% (1g Phenolphthalein + 99ml 96% Ethanol) Methyl Orange (C 14 H 14 N3 NaO 3S) – 0.1% (0.1g + 100ml DI water) Hydrochloric acid (HCl) Sodium carbonate (Na2CO3) (Standard, to calibrate the HCl concentration) Ethanol (96%) Sodium hydroxide (NaOH) (to adjust the pH of Phenolphthalein solution)
Requirements on laboratory’s basic construction African countries start late in terms of water environment monitoring, and have limited view on environment monitoring in general. Some regions even start from scratch to learn chemical laboratory construction, so it’s necessary to briefly illustrate the requirements on basic construction and personnel arrangement.
Laboratory buildings and structures: indicate some buildings or structures related to water environment monitoring, such as analysis laboratory, sample storage room, and instrument room;
Laboratory basic equipment: indicate laboratory console table, chemical cabinets, ventilation equipment, power supply system, and water supply systems; whenever necessary, such equipment may include various gas supply pipelines, pure water supply system, and air purification system.
Laboratory’s auxiliary equipment: indicate the refrigerating equipment for sample 51
storage, sampling equipment, pH meter, and balance.
Personnel arrangement in the laboratory: there should be 2-3 research and technical personnel having higher education background in terms of environment, chemistry and related subjects.
5.3.3 Pilot Water Quality Monitoring and Quality Control Pilot water quality monitoring results of Lake Tanganyika in Kigoma Region With the support of this project, NIGLAS has provided training to TAFIRI Kigoma Center’s scientists and research personnel—on multiple occasions, which equipped them with basic but better and economical means of sampling and laboratory analysis of basic water chemical parameters in lake water quality monitoring. We also signed a 2-year agreement with them so that they can conduct regular water quality monitoring (once monthly) in the lake. The monitoring was conducted at 5 sampling sites where 3 sites were within the Kigoma area and two sites located close to where the Malagarasi River enters the lake at Karago (Figure 20). Of the 5 sites, 3 were located in the near-shore areas and samples were collected to 20 m depth only; while the remaining 2 were located offshore in deep waters and samples collected to 100m depth, at the interval of 20 m to make a total of 6 layers (Figure 20). The sampling sites are set up for long-term continuous monitoring. The parameters being monitored include weather parameters (e.g. air temperature, wind speed, etc.), basic physical parameters of water (water temperature, transparency, etc.), and chemical parameters (ammonia nitrogen, nitrate, nitrite, dissolved oxygen, soluble reactive phosphorus, total nitrogen, total phosphorus, alkalinity, pH, chemical oxygen demand, primary productivity, and chlorophyll a).
52
Figure 20
Location of the pilot water quality monitoring sites in the Kigoma egion of Lake Tanganyika
The annual average values of the main parameters measured from October 2012 to February 2014 are described below: Transparency Lake Tanganyika waters have high transparencies but show both spatial and temporal variations. While the annual average value in the near-shore waters was 9.80 m and 7.85 m for Kigoma Bay and Luiche River mouth respectively (Figure 19), the minimum and maximum values for the same sites were 7 m and 11.6 m. Water transparency was even higher for the off-shore deep-water sites. While the transparency in the offshore waters outside the Kigoma Bay was 10.27 m (range: 9.0 - 11.8 m), transparency values offshore the Malagarasi River mouth averaged at 11.6m (range: 11.0 53
12.0 m). No matter whether in near-shore area or off-shore area, the value of transparency is very high, showing the basic characteristic of high transparency in oligotrophic lakes. However, the transparency in near-shore areas is relatively lower than that in offshore waters, which can be related to human activities in near-shore areas. Meanwhile, the mean annual change in transparency in the offshore areas is smaller than that of near-shore areas, indicating that the water quality of Lake Tanganyika, especially the deep offshore water areas, is very stable; while the change in near-shore areas is relatively big, indicating the impact of human activities on the water quality of the lake. 16.00
Transparency 透明度（m） (m)
14.00 12.00 10.00 8.00 6.00
MIN
4.00
AVER.
2.00
MAX
0.00 Kigoma Bay onshore
Kigoma Bay Offshore
Ujiji-Luiche onshore
Malagarasi Onshore
Malagarasi Offshore
29.00 28.00 0 Temperature 水温(oC) ( C)
27.00 26.00 25.00 24.00 MAX
23.00 22.00
MIN
21.00
AVER.
20.00 Kigoma Bay onshore
Kigoma Bay Offshore
Ujiji-Luiche onshore
54
Malagarasi Onshore
Malagarasi Offshore
(μ s/cm)s/cm） EC 电导率（μ
750 700 650 600
MAX MIN
550
AVER.
500 Kigoma Bay onshore
Kigoma Bay Offshore
Ujiji-Luiche onshore
Malagarasi Onshore
Malagarasi Offshore
14.00 12.00
DO(mg/)
10.00 8.00 6.00 MAX
4.00
MIN
2.00
AVER.
0.00 Kigoma Bay Offshore
Ujiji-Luiche onshore
Malagarasi Onshore
Malagarasi Offshore
pH
Kigoma Bay onshore
Figure 21
Mean annual value of basic environment parameters in the Kigoma region of Lake Tanganyika 55
Water temperature Figure 19 indicates that the water temperature in Lake Tanganyika has little fluctuations within a year, and differs slightly in lake areas, embodying the characteristic of very stable water temperatures. The average temperature of the 5 sampling sites fluctuated between 25.71 and 26.69℃. Temperature change in near-shore waters is smaller than that in offshore waters, mainly because of thermocline. While in the near-shore waters temperatures were measured only to 20 m deep—hence lack of obvious temperature layering, the temperatures in offshore waters were measured to a depth of up to 100 m (maximum water depth contour is around 1200m in the northern basin)—so the vertical temperature change in offshore waters is bigger than that in near-shore waters. The average annual temperature change in the near-shore and offshore waters of Lake Tanganyika were 2-3℃ and 3-4℃ respectively.
Electric conductivity and total dissolved solids (TDS) Lake Tanganyika waters have relatively high electric conductivity (EC), which is equivalent to that of Chinese eutrophic lakes such as Lake Tai, but much higher than that of lakes connecting with the Yangtze River—such as for Lake Dongting and Lake Poyang. The annual change of each point is not large, ranged between 658 and 688μScm-1. However, EC values were slightly lower in areas with the influence of river waters, especially at the Malagarasi and Luiche River mouth sites where EC values were 420.37 μScm-1 and 428.7 μScm-1, respectively.
The average TDS values ranged between 420mg/L and 476mg/L; they were relatively lower at the river mouth stations than in the main lake. The offshore waters and Kigoma Bay—which do not have extensive river inputs—had higher TDS values than those with river inputs; the average values were 450mg/L, 455mg/L and 476.42mg/L respectively. The change range of electric conductivity in near-shore waters was higher than that in offshore waters (Figure 19), indicating that near-shore waters suffer greater impact of river input and precipitation. 56
Dissolved oxygen Lake Tanganyika waters have relatively high dissolved oxygen (DO), especially the surface and sub-surface waters. According to Figure 19, the average value of dissolved oxygen in near-shore waters (including the top 20m of offshore waters) ranged between 6.2 and 9.3mg/L, while the waters at the two offshore sampling site had DO as low as 0mg/L. Actually, this anaerobic layer of water mass starts to appear at 100m depth in deep-water areas.
It can be seen from comparison of maximum value of dissolved oxygen that, the DO at the offshore sampling sites was slightly higher than that at near-shore sites, but mostly between 12.3 and 13.1mg/L, indicating relatively strong oxidation characteristic of the lake’s waters.
pH value Lake Tanganyika waters have a higher pH value than most fresh water lakes. For example, the average value of pH in the current study is higher than 9.0, with minimum and maximum values as high as 8.45 and 9.56 respectively. Furthermore, the alkalinity analysis results indicate the presence of the hydroxide (OH) precipitates, which could be inducing
NO2（mg/L)
NH4（mg/L)
the high pH value in the Lake Tanganyika waters.
57
TN（mg/L)
NO3（mg/L)
Figure 22
Average annual changes in nitrogen forms content in the Kigoma region of Lake Tanganyika
Nitrogen forms Nitrogen forms are relatively in low concentrations in Lake Tanganyika. The average concentration of ammonia nitrogen was 0.39-0.45mg/L, but the minimum value was mostly below the detection limit of the analysis method, especially in surface waters. The maximum value appeared in the deep-water areas of the two off-shore sites; where the concentrations of ammonia nitrogen below the 100 m depth reached up to 1.15mg/L and 1.30mg/L Kigoma Bay and Malagarasi sites respectively (Figure 20). The average content of nitrate ranged from 0.06 to 0.08mg/L (Figure 20), which was even lower than that of ammonia nitrogen. The content of nitrate nitrogen in offshore areas was slightly higher than that in near-shore areas, which is mainly related to the low concentration of nitrate in subsurface waters due to the metabolism of phytoplankton.
Nitrite is not the main form of nitrogen in Lake Tanganyika waters, but it forms a peak concentration at the nitrate-ammonia interphase, especially in deep-waters at the oxic-anoxic boundary where oxygen concentrations is 0mg/L. From Figure 20, it can be seen that nitrite was highest in offshore waters off Kigoma Bay than elsewhere (Figure 20), although the average value does not show such differences.
The average value of total nitrogen (TN) does not show obvious differences among sites. However, the average values at sampling sites close to river mouths were slightly higher than for offshore sites, indicating that TN input into the lake is closely related to 58
rivers, which are affected by land use. Average TN values ranged between 0.39 and 0.49mg/L (Figure 20). Contrary to average values, the maximum TN values show a definitive pattern where the near-shore sites of the two river mouths have elevated values—1.20 mg/L and 1.12 mg/L for Luiche Ujiji and Malagarasi respectively. The maximum values at the two offshore sampling sites ranged only between 0.72 and 0.85 mg/L.
Phosphorus The Lake Tanganyika waters have very low phosphate concentrations. Phosphate values were highest at the inshore site in Kigoma Bay (i.e. 0.05mg/L), and drops to 0.01mg/L in the offshore waters; while the mean value at Malagarasi were 0.004mg/L and 0.01mg/L for the near-shore and offshore sites respectively (Figure 21). These results indicate that the near-shore are somewhat enriched than offshore sites, which may indicate substantial influence of land use in the study areas.
The total phosphorus (TP) values on the other hand were between 0.04 and 0.07 mg/L on average. The minimum values were lower than the detection limit in most sites except for the two Malagarasi sites—near-shore and offshore—where values above detection limit but below 0.05 mg/L were measured (Figure 21). A similar trend can be
TP（mg/L)
P2O4（mg/L)
observed for maximum values where the Malagarasi offshore site had the highest of all.
Figure 21 Average annual changes in Phosphorus content in the Kigoma region of Lake Tanganyika
59
Monitoring method and data quality evaluation The value of the various water quality parameters obtained by the Kigoma Laboratory after sixteen (16) months of regular sampling using the above mentioned low-cost monitoring technology compares well with previous researched in the area. However, alkalinity and pH value are on the high side, while nitrate and phosphate value are low, and TN and TP show a trend of consistent change in space and time, indicating that, the monitoring method is suitable for the regular water quality monitoring of Lake Tanganyika region. After simple reconstruction and equipment supplementation of the laboratory, instruction given to laboratory personnel twice and practical operation training for more than three times, the laboratory has the ability to conduct regular monitoring.
It was also discovered from the evaluation that Lake Tanganyika has good water quality on the whole, and that some nutrients values were very low—even lower than detection limits. For example, the nitrate and phosphate values were usually hard to detect. However, these nutrients play an important role in the productivity of the lake; therefore we cannot completely cancel the monitoring on these parameters. The future plan will be to consider higher-precision detection technologies for these and other important parameters. In addition, during the early stages of the monitoring program, the concentration of ammonia nitrogen was higher than normal, primarily because of the high alkalinity of the water, which in combination with the Nessler’s reagent colorimetric method caused precipitation of some hydroxides, thereby inducing turbidity, which affected absorbance measurements. This problem was solved by decanting the treated sample and the clear solution for measuring absorbance. Also another solution would be to add salts to eliminate turbidity during measurement. For the low nutrients values in the water samples, improper sample transportation and storage could also greatly affect the measurement result.
60
5.3.4 Cost- Benefit Analysis of the Monitoring Technology Lump-sum investment of monitoring hardware cost The investment in basic analysis and test equipment may be estimated to around RMB 30,000.00 (equivalent to 5,000 USD).
Spectrophotometer: Single machine RMB10,000
Figure 24
Sterilization pot: RMB1,000
Balance: RMB1,000
Monitoring hardware cost
Cost of consumables for monitoring Before the implementation of the project, the water environment monitoring methods used by the four countries in the Lake Tanganyika Basin were mostly the fast reagent kit determination methods, which were developed and popularized by projects funded by various international organizations; most projects were short-term (between 2 and 5 years). Restricted by economic condition of aided countries, most of these high-cost reagent kit methods could not be applied continuously; therefore preventing long-term monitoring efforts in the basin. The low-cost monitoring technology used in this project adopts extremely low-cost common analytical reagents and analytic instruments produced by China. The reagents are calculated based on 8 basic water quality monitoring parameters (i.e. DO, NO3, NO2, TN/TP, CODMn, NH4, PO4). The current methods lower the cost of single sample reagent from an approximate value of RMB 116 to RMB 1.36 ($ 20 to $ 0.2 in US dollar).
61
Figure 25
Table 4
High-cost reagent kit
Comparison of single sample reagent consumption
Reagent kit method
Low-cost analysis method RMB/sample
RMB/PKG
samples/PKG
RMB/sampl
DO
1.05
380.00
25.00
e 15.20
NO3
0.01
420.00
100.00
4.20
NO2
0.02
360.00
100.00
3.60
600.00
25.00
24.00
TN/TP
0.03
1218.00
50.00
24.36
CODMn
0.20
458.00
24.00
19.08
NH4
0.04
1100.00
50.00
22.00
PO4
0.02
380.00
100.00
3.80
SUM
1.36
116.24
Cost-benefit analysis We comprehensively evaluated the several methods for water environment monitoring using the criteria of efficiency, precision and various costs (see Table 5). With consideration given to monitoring sustainability and cost effectiveness, the best scheme is low-cost monitoring.
62
Compared with low-cost monitoring, online monitoring has the lowest score; because despite the greatest superiority in monitoring frequency, the non-mobility of online monitoring induces the inferiority of this method in terms of monitoring scope. Furthermore its expenses are the highest among all monitoring methods, including monitoring cost, initial input, substantial consumable replacement, and human maintenance cost. High-precision instrument analysis has good effect, but requires quite high initial input cost and maintenance cost, and has high requirements on technical personnel and work environment, which hinders its wide use. Portable instrument monitoring also has the problem of high cost.
Table 5
Score of monitoring effect factors and monitoring cost factors
Online monitoring
Remote sensing monitoring
Fast determination monitoring with reagent kit
High-precision instrument analysis
Low-cost monitoring
3
3
0
2
2
1
Monitoring frequency
2
3
1
2
0
0
Monitoring Scope
2
0
3
2
1
1
Analysis precision
0
0
0
1
3
2
Portable sensor monitoring
Monitoring efficiency
Monitoring method
Monitoring effect
Monitoring cost
Subtotal
7
6
4
7
6
4
Initial cost
-2
-3
-3
-2
-3
0
Subsequential Cost
-2
-3
-1
-3
-1
0
Maintenance cost
-2
-3
0
-1
-3
-1
Subtotal
-6
-9
-4
-6
-7
-1
1
-3
0
1
-1
3
Total score
Notes: 1) Monitoring efficiency, defined as the time spent for obtaining monitoring results; monitoring frequency—the number of times that monitoring can be conducted in a defined period of time in order to meet the general requirement for sustainable monitoring; monitoring scope—of the spatial distribution of water areas/sites/water bodies covered in one water environment monitoring, including the water area or river length; analysis precision, defined as the accuracy and correctness of analysis result in condition of meeting the requirements of methods and standards; initial cost, refers to the minimum initial investment required to meeting the monitoring methods’ application requirements; subsequent cost, is defined as the minimum costs of consumables needed to continue monitoring as required by the monitoring method; maintenance cost, refers to the minimum manpower and maintenance cost required to meet the method requirements and with consideration given to human force and economic costs; 2) The index of effect factor is assigned to 63
be 0~3 respectively, where 0 is the minimum and 3 is the maximum. Higher score represents better monitoring effect. The index of cost factor is assigned to be 0~-3, where 0 indicates the lowest cost, and -3 indicates the highest cost. The lower score represents the highest cost.
5.4 Plan for River-Lake Integrated Monitoring
by C Yu & SS Chen
5.4.1 Monitoring Objectives Water environment quality is affected by various factors involving land use, the mutual relationship of upstream and downstream, water body types, and pollution types. The system of water environment monitoring gives priority to integrated watershed management instead of pollution control. It aims to realize human health and aqua ecosystem security, to establish and perfect the water monitoring system at watershed scale, and to strengthen the technologies in terms of the screening of monitoring objectives and parameters, the distribution of monitoring sites, the sampling and analysis methods, the data quality control, and the management of monitoring network and information.
Most African countries do not have surface water environment monitoring systems. In countries where it exists, the objective is generally to monitor the quality of drinking water sources from the angle of drinking water sanitation and security, or supervise the wastewater (mainly sewage) draining from production sectors and domestic areas; and provide evidence for pollution source management. In such countries, regular surface water monitoring is never a priority.
Lake Tanganyika is an important source of animal protein in East and Central Africa (Molsa et al 1999, 2002). The fisheries of the lake also form a profound source of economy to the riparian residents. The special species richness and the abundant biodiversity make this lake of high scientific significance, especially in organic evolution research. However, in recent years, climate change has been indicated to impact the lake, causing warming of the lake water, decline of water level, and reduction in primary 64
production—further highlighting the necessity to monitor this lake’s water environment. Comprehensively considering this region’s human activities, resource utilization demand, and basic characteristics of water environment, we have determined the objectives of regular monitoring by starting from the lake basin’s management demand and actual monitoring ability, as shown below: (1) Evaluate the threat of water quality to aquatic organism and human health; (2) Monitor the dynamics and change in the input of external nutrients; (3) Monitor the horizontal and vertical changes in lake water quality; (4) Provide evidence for making pollution control measures for maintaining a healthy river-lake water ecosystem.
5.4.2 River-Lake Monitoring Sites Layout Water System Distribution in the Lake Tanganyika Basin Lake Tanganyika basin has two large rivers which running into the lake. These are Malagarasi River and Rusizi River in Tanzania and Burundi respectively. Besides these two large inflowing rivers, there are several other small and important ones such as Lufubu and Lugufu. The only river running out of the lake is the Lukuga which is located in the Democratic Republic of Congo. Some rivers pass through urban centers, such as the River Ntahangwa in Burundi; some rivers are located in areas with relatively developed agricultural planting activities along the banks, such as the Luiche River in Tanzania. These rivers bring non-treated urban domestic sewage, agricultural pollutants, heavy metals and large quantities of sediments into the lake, affecting the lake’s water quality and ecosystem functioning. The main rivers running into Lake Tanganyika are distributed as shown in Figure 24 below.
65
Figure 26
Map of the water system running into Lake Tanganyika
(Compiled based on the data published on line at http://www.diva-gis.org, http://srtm.csi.cgiar.org) 66
Criteria for selecting monitoring sites Monitoring sites are selected by preferentially referring to the locations with high potential as pollution sources, as provided in other reports (Chen et al., 2010), including areas where domestic sewage, industrial wastewater and agricultural effluents flow into the lake. Under such circumstances, the bayou of big rivers running into the lake is given first priority. The areas where ships are anchored and maintained should also be listed into the scope of first type, since here, pollutants such as petroleum and lubricant will possibly flow into the lake. The areas where petroleum naturally seeps into lake or ground surface are similarly considered as important monitoring points, and such points are also listed into the first type. In addition, protection zones are included in the priority category since the water quality data from these areas could provide baseline water quality values which can be used to show pristine conditions of the lake. Such areas may include, for Lake Tanganyika, the Gombe and Mahale National Parks in Tanzania, and Nsumbu National Park in Zambia. The second category of monitoring sites includes the mouth of the second largest rivers running into the lake, ports, and fishermen’s camps/villages/landing beaches. Fishermen’s camps/landing beaches are listed into the second category of monitoring sites because there are gasoline and lubricant materials, dense population, but lack of sufficient sanitation facilities. The third type of monitoring points are located at the upstream of rivers running into the lake, especially in Tanzania where the river system catchment area accounts for about 80% of the whole basin. So the river water cannot be ignored in the monitoring program they transport materials into the lake and can help delineate the source of pollution going into the lake.
Layout of regular monitoring sites Considering local water environment monitoring ability, we further determined priority monitoring sites according to the lake’s biological and hydrodynamic environment, and the common characteristics of pollution spreading.
67
First-level priority sites: include the mouth of all major rivers running into the lake, towns and ports, and protected areas (such as Mahale, Gombe and Nsumbu National Parks). For the countries with fewer rivers running into the lake, the mouth of two rivers with the largest quantity of water may be taken as the first-level priority monitoring points, in order to monitor the pollutants diffusing into and water quality change from river mouth to offshore lake areas. In addition, monitoring points should be set up at the boundary of each country, in order to distinguish cross-boundary water quality change.
Second-level priority points: Include the mouth of some small rivers running into the lake, fishermen’s camps/villages/landing beaches, and upstream sections of main rivers running into the lake. In condition of limited monitoring ability, it’s not viable to set up regular monitoring points in these areas.
Distribution of monitoring points in Burundi A total of 8 monitoring points were set up in Burundi. There are four first-level monitoring points: The lake-inlets of the Ruzizi River (Burundi), Ntahangwa River (which flows through Bujumbura’s Center), Bujumbura port and the south lake center area in Burundi area of Lake Tanganyika.
There are four second-level monitoring points: The mouth of Mugera River and Kanyosha River, Rumonge Fishermen’s Wharf and Muha residential points.
Distribution of monitoring points in the Democratic Republic of Congo The Democratic Republic of Congo owns 45% of the lake shoreline, so here, 14 monitoring points can be set up.
There are 8 first-level monitoring points: Lake-inlet of Ruzizi River (DRC), the lake-inlet of Lukuga River in Kalemi Town, Luhanga and Pemba Protected areas, Uvira Port, Kalemi Port, and Baraka Town at Burton Bay (natural petroleum leakage). 68
There are 6 second-level monitoring points: The lake-inlet of Kavimvira River in Uvira, the lake-inlet of Mulongwe River, the inlet of Kalimabenge River, the lake-inlet of Mtambaba River, Moba Town and Moliro Fishermen’s wharf.
Distribution of monitoring points in Zambia A total of 7 monitoring points are designed in Zambia.
There are four first-level monitoring points: Mpulungu Port, lake-inlet of Lufubu River, offshore lake center area, and Nsumbu National Park.
There are three second-level monitoring points: Lueche River mouth, Lunzuwa River mouth, and Chisala River mouth.
Distribution of monitoring points in Tanzania There are a total of 17 monitoring points set up in Tanzania, since Tanzania owns a large part of the Lake Tanganyika basin in terms of watershed.
There is a total of 10 first-level monitoring points: The Kigoma Bay near-shore and offshore areas, Malagarasi River mouth, Luiche River mouth, Katonga and Ujiji fishermen’s, Ruchugi River (Pwaga), Kasanga Port and Kalambo Port, Gombe and Mahale National Park, and Mpfumu fishermen’s village in Lake Nyamagoma.
There are 7 second-level monitoring points: Kagunga fishermen’s village, Ikola fishermen’s village, Kipili fishermen’s village, Kivumba fishermen’s village (in Sagara Lake), Kalya fishermen’s village, Ugalla River (at Kaliua Railway Bridge), and Karago fishermen’s village.
The water quality monitoring network distribution is as shown in Figure 27. 69
Figure 27 Map of Lake Tanganyika showing the distribution of water quality monitoring sites
70
Distribution of monitoring sites in offshore areas The offshore monitoring sites are included in the four countries’ monitoring networks. These are first-level monitoring sites which are located in offshore areas of the lake in Burundi, the Democratic Republic of Congo, Zambia, and Tanzania. Samples will be obtained
in the form of a profile where waters will be drawn from the following depth
layers: 0m, 20m, 40m, 60m, 80m, 100,m, 110m, 120m, 140m, 160m, 180m, 200m, 600m and 1000m.
5.4.3 Monitoring Parameters and Frequency of Rivers Running into the Lake The water quality monitoring results in previous special researches show that, Lake Tanganyika and the rivers running into and out of it generally have good water quality. Nitrogen and phosphorus nutrients are relatively low, and heavy metals are generally lower than detection value. While alkalinity and pH value of the water are on the high side; some lake bays and urban rivers show symptoms of eutrophication. Moreover, the content of suspend sediments flowing into the rivers and the lake is relatively high.
On this basis, rivers monitoring, which was conducted once every month, included physical and chemical parameters such as water temperature, transparency, alkalinity, pH, salinity, dissolved oxygen, electric conductivity, oxidation-reduction potential, turbidity; mineralization, nutrients (TN, TP, nitrate nitrogen, nitrite nitrogen, ammonia nitrogen, and phosphate), suspended solids (SS), and heavy metals.
5.4.4 Monitoring Parameters and Frequency in the Lake The main water quality parameters for monitoring in the lake include: physical and chemical parameters such as water temperature, transparency, alkalinity, pH, salinity, dissolved oxygen, electric conductivity, oxidation-reduction potential, turbidity; mineralization, nutrients (TN, TP, nitrate nitrogen, nitrite nitrogen, ammonia nitrogen, 71
and phosphate, DTN, DTP, CODMN); and chlorophyll a. Similarly, the monitoring is conducted once every month.
5.4.5 Atmospheric Deposition Monitoring Four sites are planned to set up for atmospheric deposition monitoring, which are located at Bujumbura (Burundi), Kigoma (Tanzania), Kalemi (D.R.Congo), and Mpulungu (Zambia) respectively. It’s necessary to collect dry and wet samples of atmospheric sedimentation, and record the precipitation time and frequency, air temperature, humidity, wind speed, and rainfall. The wet and dry atmospheric sedimentation monitoring parameters include TN, TP, nitrate nitrogen, ammonia nitrogen, nitrite nitrogen and phosphate. This type of monitoring is conducted once a month for dry deposition, and every rainy day for the wet atmospheric sedimentation.
5.5 Basin Data Integration and Management
by C Yu & JF Gao
5.5.1 Database Structure and Data Source Based on the characteristics of the ecologic system in the lake basin, and in order to provide data support for the decisions making on comprehensive water environment management, we have determined the data contents in four aspects: basic geographic information, meteorological and hydrological records, water quality monitoring data, and social & economic data. Generally, the data sources include archive documents and data, real-time monitoring data and spatial data produced by the project. Basic geographic information data were mainly sourced from relief maps, and network data sharing from the European Space Agency. Meteorological and hydrological records were sourced from the records of local meteorological and hydrological stations; while water quality monitoring data were collected from the regular monitoring of fixed station, and various documents from previous international project. The social and economic data were 72
mainly sourced from local statistical yearbooks, general survey data and special investigation documents from previous projects.
The database is divided into three sub-databases according to content and form. The first is filing resource and information database, including basic geographic information and social & economic data, classified and stored by country/region and parameter; the second is meteorological and hydrological database, stored by country/region and parameter; and the third is water quality database, stored by regular monitoring data and investigation data.
Basic geography data structure The lake’s basic geography data structure is as shown in Table 6:
Table 6 Lake’s basic geography data structure
No.
Data item name
Storage
Type
Length
name 1
Name (Chinese)
MCC
Text
20
2
Name (English)
MCE
Text
20
3
Alternative name
BC
Text
20
4
Code
DM
Text
20
5
The basin subordinated to
LSLY
6
Administrative area subordinated to
LSXZ
7
Lake area subordinated to
LSHQ
Text
20
8
Longitude
JD
Numerical
10
Text Text
20 20
value 9
Latitude
WD
Numerical
10
value 10
Type
LX
Text
20
Physical geography data structure The lake’s natural attribute data structure is as shown in Table 7: 73
Unit
Remark
Table 7 Lake’s natural attribute data structure
No.
Data item name
Storage
Type
Length
Unit
Remark
name 1
Name (Chinese)
MCC
Text
20
2
Code
DM
Text
20
3
Area
MJ
Numerical
10
km2
4
Volume
10
0.1 billion
value RJ
Numerical value
5
Altitude (average water level)
HB
Max. length
ZDCD
6
Numerical
m3 10
m
10
m
10
m
10
m
10
m
10
km
value Numerical value
7
Average length
PJCD
Numerical value
Max. width
8
ZDKD
Numerical value
9
Average width
10
Shoreline length
PJKD
Numerical value
AXCD
Numerical value
Shoreline development coefficient
AXXS
12
Type
LX
Text
20
13
Island area
DYMJ
Numerical
10
11
Numerical
10
value
km2
value Island rate
14
DYL
Numerical
10
value
Air temperature data structure is as shown in Table 8: Table 8 Air Temperature Data Structure
No.
Data item name
Storage
Type
Length
Unit
name 1
Name (Chinese)
MCC
Text
20
2
Code
DM
Text
20
3
Annual average temperature
PJQW
Numerical
10
℃
10
℃
4
air
Max. air temperature
value ZGQW
Numerical value 74
Remark
5
Min. air temperature
ZDQW
Numerical
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
10
℃
value 6
7
Average temperature January Average temperature
YIY
of
Numerical value
ERY
of
Numerical value
February 8
Average temperature
SANY
of
Numerical value
March 9
Average temperature
SIY
of
Numerical value
April 10 11 12 13
Average temperature of May Average temperature of June Average temperature of July Average temperature of
WUY
Numerical value
LIUY
Numerical value
QIY
Numerical value
BAY
Numerical value
August 14
Average temperature
JIUY
of
Numerical value
September 15
Average temperature
SHIY
of
Numerical value
October 16
Average temperature
SHIYIY
of
Numerical value
November 17
Average temperature
SHIERY
of
Numerical value
December
Rainfall data structure is as shown in Table 9:
Table 9 Rainfall Data Structure
No.
Data item name
Storage
Type 75
Length
Unit
Remark
name 1
Name (Chinese)
MCC
Text
20
2
Code
DM
Text
20
3
Multi-year average rainfall Rainfall of January
DNJJY
Numerical
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
4
value YIY
Numerical value
5
Rainfall of February
ERY
Numerical value
6
Rainfall of March
SANY
Numerical value
7
Rainfall of April
SIY
Numerical value
8
Rainfall of May
9
Rainfall of June
WUY
Numerical value
LIUY
Numerical value
10
Rainfall of July
QIY
Numerical value
11
Rainfall of August
BAY
Numerical value
12
Rainfall of
JIUY
Numerical
September 13
Rainfall of October
14
Rainfall of
value SHIY
Numerical value
SHIYIY
Numerical
November 15
Rainfall of
value SHIERY
Numerical
December
value
Evaporation data structure is as shown in Table 10: Table 10 Evaporation Data Structure
No.
Data item name
Storage
Type
Length
Unit
name 1
Name (Chinese)
MCC
Text
20
2
Code
DM
Text
20
3
Multi-year average evaporation Evaporation of January
DNJZF
Numerical
10
4
value YIY
Numerical value 76
10
mm
Remark
5
Evaporation of
ERY
February 6
Evaporation of Evaporation of
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
10
mm
value SANY
March 7
Numerical Numerical value
SIY
April
Numerical value
8
Evaporation of May
9
Evaporation of
WUY
Numerical value
LIUY
June 10
Numerical value
Evaporation of July
QIY
Numerical value
11
Evaporation of
BAY
August 12
Evaporation of
value JIUY
September 13
Evaporation of
14
SHIY
Evaporation of
Numerical value
SHIYIY
November 15
Numerical value
October
Evaporation of
Numerical
Numerical value
SHIERY
December
Numerical value
Solar radiation aggregate data structure is as shown in Table 11: Table 41 Solar Radiation Aggregate Data Structure
No.
Data item name
Storage
Type
Length
Unit
name 1
Name (Chinese)
MCC
Text
20
2
Code
DM
Text
20
3
Multi-year average radiation quantity
DNJFS
Numerical
10
Kilocalorie/cm2
Radiation quantity of January
YIY
10
Kilocalorie/cm2
Radiation quantity of February
ERY
10
Kilocalorie/cm2
Radiation quantity of March
SANY
10
Kilocalorie/cm2
7
Radiation quantity of April
SIY
10
Kilocalorie/cm2
8
Radiation quantity
WUY
10
Kilocalorie/cm2
4 5 6
value Numerical value Numerical value Numerical value Numerical value Numerical 77
Remark
of May
value
Radiation quantity of June
LIUY
Radiation quantity of July
QIY
11
Radiation quantity of August
BAY
12
Radiation quantity of September
JIUY
Radiation quantity of October
SHIY
Radiation quantity of November
SHIYIY
Radiation quantity of December
SHIERY
9 10
13 14 15
Numerical
10
Kilocalorie/cm2
10
Kilocalorie/cm2
10
Kilocalorie/cm2
10
Kilocalorie/cm2
10
Kilocalorie/cm2
10
Kilocalorie/cm2
10
Kilocalorie/cm2
value Numerical value Numerical value Numerical value Numerical value Numerical value Numerical value
Water environment data structure Water environment data structure is as shown in Table 12: Table 52 Water Environment Data Structure
No.
Data item name
1
Name (Chinese)
2
Code
3
Storage
Type
Length
MCC
Text
20
DM
Text
20
Monitoring point number
CDH
Numerical
10
Longitude
JD
Unit
name
4
value Numerical
10
Degree
10
Degree
10
YYMMDD
10
℃
10
m
value 5
Latitude
WD
Numerical value
6
Sampling date
7
Water temperature
CYRQ
Numerical value
SW
Numerical value
8
Transparency
TMD
Numerical value
9
pH value
PH
Numerical
10
value 10
dissolved oxygen
RJY
Numerical value 78
10
mg/L
Remark
11
electric conductivity
DDL
Numerical
10
μs/cm
10
Degree
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
value 12
Chroma
SD
Numerical value
13
Mineralization
KHD
Numerical value
14
Suspended substance
15
Chloride
XFW
Numerical value
LHW
Numerical value
16
Sulfate
LSY
Numerical value
17
alkalinity
JD
Numerical value
18
Calcium
CA
Numerical value
19
Magnesium
20
Potassium
MG
Numerical value
K
Numerical value
21
Sodium
NA
Numerical value
22
Total hardness
ZYD
Numerical value
23
Total phosphorus
TP
Numerical value
24
Total nitrogen
25
Nitrate nitrogen
26
Nitrite nitrogen
TN
Numerical value
XSYN
Numerical value
YXSYN
Numerical value
27
Ammonia nitrogen
AN
Numerical value
28
Permanganate index
GMSY
Numerical value
29
TOC
TOC
Numerical value
30
chlorophyll a
31
BOD5
Chla
Numerical value
BOD5
Numerical value 79
Cadmium
32
CD
Numerical
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
10
mg/L
value Lead
33
PB
Numerical value
Copper
34
CU
Numerical value
35
Total mercury
36
Total arsenic
THG
Numerical value
TAS
Numerical value
37
Hexavalent chromium
CR6
Numerical value
38
Total chromium
TCR
Numerical value
Ferrum
39
FE
Numerical value
40
Nabgabese
41
Zinc
MN
Numerical value
ZN
Numerical value
Data storage format The specification of data storage format is as shown in Table 13. Table 63 Data Storage Format
No.
Data Type
Storage Format
1
Remote sensing
ERDAS IMG format
image 2
Vector layer
ARCGIS Shapefile format
3
Text data
ACCESS format
4
Other images
JPG format
5
Multimedia
WAV, AVI, MPEG, MP3
6
Digital elevation
ARCGIS Grid format
7
Data set
ACCESS, ARCGIS Geodatabase format
80
5.5.2 Data Management Platform The Lake Tanganyika Basin water environment monitoring data management system adopts the data management model platform based on database structure; and its core objective is to construct a scientific basin monitoring database, which has a safe structure and stable operation. According to the objective and content of system construction, and through system development and integration, the Lake Tanganyika Basin water environment monitoring data management system has the functions of storage, inquiry, analysis, maintenance and issuance of multi-source data such as the basin’s hydrological and meteorological data, water quality data, attribute data, and metadata.
Data collection and entry The collection and entry of newly acquired data includes the input, storage and processing of multi-source data such as the monitoring data and attribute data, the input and editing of geospatial data; and there is a data quality assurance and quality control mechanism.
Data browsing and inquiry The lake basin’s data management system has a data inquiry and analysis function; which can, according to user-defined conditions, find data meeting the specified conditions quickly and correctly. Meanwhile, according to users’ new inquiry demands emerging endlessly, the system could expand the inquiry function with ease and flexibility.
Operating environment The database uses Windows Server 2003 supporting system clusters. The use of environment considers the application demand of Lake Tanganyika Basin water environment monitoring end-users.
According to the scale of data in the Lake Tanganyika Basin water environment monitoring database, and the analysis on potential application demand, the database 81
system platform adopts a simple and highly-efficient ACCESS 07 database. ACCESS database is featured by security, recoverability, extendibility, and usability, supports the development and application of professional GIS, and supports the seamless upgrading to enterprise-level large databases such as Oracle and SQL Server.
5.5.3 Data Use and Updating Data use is implemented through network search and application at present. At the special website of the project (http://www.protect-lake-tanganyika.cn/eindex.aspx), data sharing column will be set up, and all metadata information will appear in this column. After retrieving the data in the column and obtaining corresponding file name, users may obtain the authority to use the data by filing an application. The administrator will log onto the management platform’s back end to update and maintain the database, and update metadata information.
6 Conclusions
by SS Chen
The main objectives of this research were to raise the water quality monitoring ability of the countries surrounding Lake Tanganyika; to develop a set of water quality monitoring technology and management schemes suitable for developing states; and to promote sustainable lake water environment protection and the basin’s sustainable development. For over 3 years of the project implementation phase, we have conducted investigations, research and experiment from which we were able to: i) clearly describe basic characteristics of the regional land and socio-economic development; ii) summarize the overall characteristics of the water quality of, and main rivers flowing into, the lake; iii) assessed and revealed key advantages and disadvantages of various river-lake basin water quality monitoring technologies and; iv) through a pilot demonstration project, we planned and established river-lake integrated water quality monitoring station; v) developed low-cost applicable monitoring technology, and data management system framework, which could be directly applied to monitor the water quality of Lake 82
Tanganyika Basin, and provide reference for similar regions.
The results of research on land use, river-lake water quality, monitoring technology and application are summarized as follows: 1) The remote sensing investigations on the current state and change in land use along the lake, indicate that: i) forest land accounts for a big proportion (70%); ii) that most areas are in non-developed or low-developed state; iii) farmland accounts for about 20% of the total land cover, which are mostly distributed around cities, and relatively concentrated along the lake shoreline. The results also show that the proportion of each land use type did not change significantly over the past ten years (i.e. 2001-2011). Moreover, during the ten year period some changes occurred which include the decrease in forest land area, and increase in farmland and artificial surfaces. However, the cities grew by less than 40 km2 for the entire period which did not match with the population increase of about 500,000 and the growth rate of more than 3% in the region. Field investigations also discovered a slow growth of cities along the Lake, with these cities having low level of infrastructure construction, and in severe shortage of environmental protection facilities, despite having high population densities. All these form potential threat to the lake’s environment and its water quality.
2) The pilot water quality monitoring results of northern Lake Tanganyika waters, the main rivers and their tributaries flowing into the lake, and other water bodies within the basin indicate low nutrients concentrations, high alkalinity and pH value, and below detection limits levels for heavy metals. These results were also compared to previous water quality monitoring developed and used around the lake. According to the pilot monitoring on the water quality of rivers and Lake Tanganyika in Tanzania, the TN and TP concentrations were higher in rivers than in the lake, and the concentrations in the near-shore site were higher than the offshore waters, indicating that rivers are important source of nutrients into the lake. On the other hand, the northern section of the Malagarasi basin, where agricultural activities are frequent, the concentration values of 83
both TN and TP were highest than in the southern section of the main river, indicating that agricultural activities have significant contributions to the organic substances in rivers.
3) Kigoma Laboratory has carried out 2-years of regular water quality monitoring using the low-cost monitoring technology formed by the project. The data and results from the monitoring were subjected to stringent quality checks and were verified to be reasonably close and within the normal ranges. Also it was found that the Kigoma Laboratory can maintain long-term monitoring activities in terms of mechanism, indicating that the monitoring technology is suitable for the regular water quality monitoring in the Lake Tanganyika region. The laboratory was renovated by the PRODAP project and supplemented with equipment by this project through NIGLAS. After a round of closely monitored instructions and 2-3 times of practical training on proper operations of the donated equipment, the laboratory personnel were able to maintain a regular water quality monitoring. Moreover, the evaluation of the monitoring data and methods indicated that some nutrients were very low—even lower than the minimum detection limits of the methods. It is important therefore to pay special attention to the detection precision of some methods.
4) In terms of a sustainable monitoring program development, we determined the objective and parameters for regular monitoring, and selected parameters which were easy-to-measure. However, monitoring parameters specially related to the health of human and other organisms, such as pesticides and caffeine, require more complicated technologies and equipment; therefore they were not included as regular monitoring items.
5) During database construction it was discovered that this region lacks basic environmental data, and does not have a uniform platform for collection and management of data, which makes it very difficult to find and share data among 84
stakeholders.
6) In addition, at present, most of the countries in the study area have low economic development level, and local scientific research personnel have extremely low income, while the reward paid under international cooperation projects is very high, and has great difference from the local income. This is also an important reason why it’s difficult for long-term continuation of monitoring works after termination of such projects.
85
References Antonio D.G, Louisa J.M. (2005).Land cover classification system, Environment and Natural Resources Series 8,FAO,ROME. APHA (1998). Standard Methods for the Examination of Water and Wastewater, eighteenth ed. American Public Health Association, Washington, DC. Chale, F.(2000). Studies in Tanzanian Waters. Pollution Special Study. Pollution Control and Other Measures to Protect Biodiversity in Lake Tanganyika (UNDP/GEF/RAF/92/G32):20. Chen, S.S., Nkotagu, H.H. and Muderhwa, N. (2010). Water quality and monitoring needs of Lake Tanganyika. Brief Report of Desk Study for Enhance the Capacity of Monitoring Shared Water Resources of Lake Tanganyika Program. NIGLAS in Nanjing of China: 29pp. Coenen, E., Hanek, G., Kotilainen, P. (1993). Shoreline classification of Lake Tanganyika Based on the Results of an Aerial Frame Survey. FAO/FINNIDA Research for the Management of the Fisheries on Lake Tanganyika, GCP/RAF/271/FINTD/10 (En): 11 p. Coulter, G. W. (1991). Lake Tanganyika and its life. Natural History Museum Publications, Oxford University Press, London, Oxford, New York: 1-6. Descy, J. P., Higgins H. W., Mackey D. J., et al. (2000). Pigment ratios and phytoplankton assessment in northern Wisconsin lakes.Journal of Phycology 36(2): 274-286. Foxall, C., Chale F., Bailey-Watts A., et al.(2000). Pesticide and heavy metals in fish and molluscs of Lake Tanganyika.Pollution control and other measures to protect biodiversity in Lake Tanganyika (RAF/92/G32), funded by the United Nations Development Program/Global Environment Facility. Gong Pan, Chen Zhongxin, TangHuajun (2006). Progress of the research on classification system of land vegetation. Chinese Journal of Agricultural Resources and Regional Planning 27(2):35-40.
86
Hutchinson, G. E. (1975). A Treatise on Limnology: Vol. I, Geography, Physics, and Chemistry; Pt. 2, Chemistry of Lakes, Wiley. IGBP (1990). The International Geosphere–Biosphere Programme: a study of global change—the initial core projects. IGBP Global Change Report No. 12, International Geos-phere–Biosphere Programme, Stockholm, Sweden. Jarvinen, M., Salonen,K., Sarvala, J. (1999). The stoichiometry of particulate nutrients in Lake Tanganyika -implications for nutrient limitation of phytoplankton. Hydrobiologia 407: 81-88. Molsa, H., Reynolds J. E., Coenen E. J. (1999). Fisheries research towards resource management on Lake Tanganyika. Hydrobiologia(407): 1-24. Molsa, H., Sarvala J., Badende S., Chitamwebwa D. (2002). Ecosystem monitoring in the development of sustainable fisheries in Lake Tanganyika. Aquatic Ecosystem Health & Management 5(3): 267-281. Naithani, J., Plisnier P. D., Deleersnijder E. (2007). A simple model of the eco‐ hydrodynamics of the epilimnion of Lake Tanganyika.Freshwater Biology 52(11): 2087-2100. Naithani, J., Plisnier P. D., Deleersnijder E. (2011). Possible effects of global climate change on the ecosystem of Lake Tanganyika. Hydrobiologia 671(1): 147-163. Nkotagu, H. and K. Mwambo (2000). Hydrology of selected watersheds along the Lake Tanganyika shoreline. Lake Tanganyika Biodiversity Project, Technical Research Report. Ngonyani, C. J. and O'Reilly, C. M. (2002). Preliminary investigation on physical chemical characteristics (limnological parameters) of the lower Malagarasi wetland and upper Luiche River waters. NYANZA project report. http://www.geo.arizona.edu/nyanza/pdf/Ngonyani.pdf O'Reilly, C. M., Alin, S. R., Plisnier P.-D., et al. (2003). Climate change decreases aquatic ecosystem productivity of Lake Tanganyika, Africa.Nature 424(6950): 766-768.
87
Plisnier, P. (2004). Probable impact of global warming and ENSO on Lake Tanganyika.Bull. Séanc. Acad. r. Sci. Outre-mer 50: 185-196. Kimirei, I. A. , Mgaya Y. D. (2007). Influence of environmental factors on seasonal changes in clupeid catches in the Kigoma area of Lake Tanganyika. African Journal of Aquatic Science 32(3): 291-298 State Environment Protection Administration (2002). Environmental quality standard for surface water, China. Beijing (PR China): General Administration for Quality Supervision, Inspection and Quarantine of PR China. GB 3838-2002. Sichingabula, H. (1999). Analysis and results of discharge and sediment monitoring activities in the southern Lake Tanganyika basin, Zambia. Special study on sediment discharge and its consequences. Pollution control and other measures to protect biodiversity in Lake Tanganyika (RAF/92/G32). Tierney, J. E., Mayes M. T., Meyer N., et al.(2010). Late-twentieth-century warming in Lake Tanganyika unprecedented since AD 500. Nature Geoscience 3(6): 422-425. Verburg, P., Hecky R. E., Kling H. (2003). Ecological consequences of a century of warming in Lake Tanganyika. Science 301(5632): 505-507. Verburg, P. H., Van De Steeg J., Veldkamp A.,et al.(2009). From land cover change to land function dynamics: a major challenge to improve land characterization. Journal of environmental management 90(3): 1327-1335. West, K.(2000). Pollution control and other measures to protect biodiversity in Lake Tanganyika(RAF/92/G32),funded by the United Nations Development Program/Global Environment Facility.
88
Appendix: Propaganda and Achievements Issuance Presentation in the 7th Great Lakes Of the World in Bujumbura On June 17-20, 2012, the 7th Great Lakes Of the World (GLOW7) was held in Bujumbura, the capital of Burundi—one the riparian state in the Lake Tanganyika basin. The speech entitled: “Environmental monitoring for sustainable management: examples from the Lake Tanganyika Basin”, jointly signed by the scholars from UNDP/GEF, NIGLAS and LTA, and orally issued by LTA’s representative reported a part of the project achievements; and the work developed by NIGLAS around Lake Tanganyika Basin which concerns water environment monitoring and management since 2008.
China Daily (English Version) reports On Sept. 19, 2012, China Daily reported the Lake Tanganyika project, and introduced the project as a typical case of China’s technological cooperation and communication with the third world countries.
Presentation in the 2nd South-South Cooperation Conference on Coping with Climatic Change with Technology in Guangzhou of China In October 2012, Guangzhou the project principal investigator and African partners attended the conference on Strengthening South-South Cooperation on Science and Technology to Address Climate Change and Technical Training jointly organized by the Ministry of Science and Technology of China, UNDP, UNEP, and UNESCO, and issued an oral speech entitled: “Watershed management of water quality in the context of urbanization and the case study of south-south technological cooperation”.
Presentation in the Regional Consultative Conference on “Lake Tanganyika Water Quality Monitoring Ability” in Bujumbura In January 2013, the project principal investigator was invited to attend the Regional 89
Consultative Conference on “Lake Tanganyika Water Quality Monitoring Ability Construction” organized by UNEP and LTA; and gave an oral speech entitled: “Water Quality Monitoring of the Lake Tanganyika Basin: Experiences and Challenges”.
Tanzania National TV Station (ＴＢＣ) and Tanzania Independent TV Station (ITV) report the listing of Demonstration Laboratory In August 2013, the plaque was unveiled at the TAFIRI Kigoma Center Laboratory with inscriptions that designates it as the Demonstration Laboratory for water quality and ecosystem management in the East African Great Lakes region. At this occasion, the Tanzania Broadcasting Corporation (ＴＢＣ), which is the national television station, and Independent TV Station (ITV) were present and reported this event jointly.
90
