Using GIS for Modeling a Spatial DSS for Industrial Pollution in Egypt

Geographic Information Systems (GIS) have become an effective tool for decision support. Spatial Decision Support System (SDSS) is a relatively new field developed based on Geographic Information System (GIS) and Decision Support System (DSS). SDSS will be an important component of DSS applications in future. This trend will be driven by the relevance of spatial information as a component of the information needed for a wide range of decisions. This class of DSS will make an important contribution, not because of its use of the latest technology, but because it will allow decision makers incorporate a spatial dimension in their decision making. So Spatial Decision Support Systems (SDSS) are decision support systems where spatial properties of the data to be analyzed play a major role in decision making special in many sectors. Maps and geographic features can be used to show decision related information and relationship between objects to solve important problems like in spreading diseases and industrial pollution.


Introduction
The continuing development of DSS applications requires that new technologies be exploited to allo w new classes of decision be supported. Th is paper discusses the use of a Geographic Information System (GIS) as a Decision Support System (DSS) generator to create Spatial Decision Support Systems (SDSS). A geog raph ic in format ion system is a system design ed to captu re, store, man ipu late, an aly ze, manage, and present all types of geographical data. The GIS is sometimes used for geographical information science or geospatial in fo rmat ion studies to refer to the academic discipline o r career o f working with geographic information systems [1]. Geographic informat ion science is the science u n d erly in g g eo g raph ic co n cep ts , ap p licatio n s , and systems [2]. The GIS imp lementation stage consisted of the creation of a relational database, including a co mplete set of georeferenced data, and the elaborat ion of user interface for spatial analysis using GIS facilit ies in order to establish a Decision Support System (DSS). The current development of how to use models is shifting fro m indiv idual modeling techniques toward finding the synergies between modeling techn iqu es an d co mb in in g th em, to so lve real wo rld problems [3]. Integrated computer modeling enables far mo re variables to be taken into account than is possible otherwise.
The choice for dynamic modeling has been made because important driv ing fo rces and processes change over time, and actions and developments that have taken place are very often not reversible; indicating a path-dependency of developments [4].GIS allows the integration of geographical referenced data, together with local knowledge in relat ional databases to accurately display complex interactions in simp le formats [5]. By comb ining geographic data from a wide range of sources, managers can quickly assemble custom maps to expedite a range of management activ ities.
This process of creating new GIS outputs from existing data is referred to as modeling a spatial decision support system. Such modeling converts existing datasets into new datasets by applying Decision Support System (DSS) Models, Expert System (ES) and Artificial Intelligence (AI). This combination of data can create a model output that helps answer questions posed with spatial relevance. Typically, this is not modeling in terms of integrated model. GIS data presentation is of considerable value when spatial and visual representation is important.
The evolution of the DSS may be d ivided into four generations: the first DSS generation focused on data; the second DSS generation focused on improving the user interface; the third DSS generation focused on models and the fourth, the present-day generation, was obtained by introducing new analytical web-based applications [6]. As a short conclusion, the Decision Support Systems belong to a mu ltid isciplinary environment, including database research, artificial intelligence, human-co mputer interaction, simu lati on methods, software engineering and teleco mmunicat ions.
Thus, the concept of Decision Support Systems is an almost established concept, but which is still growing due to the integration (incorporation) of several ind ividual and relatively newer technologies (object orientation, expert systems, advanced communications), fro m which it ext racts new valences and strengths. Concurrently, the v itality of the concept is stimulated by the growing tendency of integrating processes and functions with all industrial systems, environment management systems, etc. The systems that used to provide support in the decision process have been named by specialists Decision Support Systems or Decision Management Systems. Recently, terms such as artificial intelligence, data mining, on-line analytical processing, and knowledge management have been used for systems whose objective was to inform and assist managers in the decision process.
Spatial Decision Support Systems (SDSS) are decision support systems where spatial properties of the data to be analyzed p lay a major role in decision making special in health sector. Maps and geographic features can be used to show decision related informat ion and relationship between objects to solve important problems like in the relation between some industrial pollution and some d iseases. Based on the understanding that SDSS is a kind of information system capable of provid ing spatial decision making schemes to decision-makers, we present our paper to helping decision makers in Egypt. So SDSS is an interactive, computer-based system designed to support a user or group of users in achieving a higher effectiveness of decision making while solving a semi-structured spatial problem [7]. It is designed to assist the spatial planner with guidance in making land use decisions. SDSS typically uses a variety of spatial and nonspatial in formation, like data on land use, transportation, water management, demographics, agriculture, climate or e mp loy ment.

Spatial Decision Support System
SDSS typically uses a variety of spatial and nonspatial informat ion, like data on land use, transportation, water management, demographics, agriculture, climate or emp loyment. By using two or more known points in h istory the models can be calibrated and then projections into the future can be made to analyze different spatial policy options. Using these techniques spatial p lanners can investigate the effects of different scenarios, and provide informat ion to make informed decisions. To allow the user to easily adapt the system to deal with possible intervention possibilities an interface allo ws for simple modification to be made. Managers use computerized decision support systems for accessing important information, the principles of decision making, modeling and how business intelligence tools are used to support decision making [8].
GIS database management systems (DBM Ss) are designed to support cartographic display and spatial query. Database of an SDSS must support cartographic display, spatial query and analytical modeling by integrating three types of data: 1. locational (spatial p rimit ives such as coordinates and chains) 2. Topological (attribute-bearing objects, e.g. points, nodes and lines, and relat ionships between them) 3. Thematic (attributes of the topological objects, including population, elevation, and vegetation).
Database must permit the user to construct and exploit complex spatial relations between all three types of data at a variety of scales, degrees of resolution and levels of aggregation. Database management systems found in many GIS use the relational data model. However, alternative data models have proved effective in applications of DSS. The extended network model is an enhanced form of the network model and is effective for representing the links and nodes of transportation networks. Transportation networks are a popular base for developing SDSS because of the importance of applications for site selection and the abundance of methods of analysis.
Multi-Criteria Decision Analysis methods support decision-makers in analy zing a set of alternative spatial solutions, such as the most likely ecological habitat for restoration, against multip le criteria, such as vegetation cover or roads. MCDA uses decision rules to aggregate the criteria, which allows the alternative solutions to be ranked or prioritized [9].

Water Pollution
The protection of water fro m pollution represents another important priority. The EEAA in this respect, Law 4/1994 for the Environ ment places an emphasis on the protection of the coastal wasters and the marine environ ment, complementing Law 48/ 1982 for the protection of the River Nile[10]. The lines of action in this regard encompass water quality monitoring activit ies and initiatives, as well as pollution abatement and mit igation efforts [11].
The protection of water resources is one of the most critical environ mental issues in Egypt. Egypt is facing an increasing demand for water due to the rapid ly growing population, as well as the growth in urbanization, agricu lture and industry. Pollution fro m do mestic sources, despite rapid population growth in Egypt, the percentage of the population with access to municipal water supplies has also increased due to major investment in the water sector. Around 96 % of households in urban areas and almost 94% in rural areas have access to piped water. As for access to sanitation, the urban governorates have the highest sanitation coverage (around 98%), while in rural governorates access to sanitation is 91%. Pollution fro m industrial sources, these pollutants are generated primarily fro m heavy engineering, electroplating and chemical industries, such as pesticides manufacturers, and petroleum refineries. Certain types of significantly polluting industries have a specific geographical distribution, such as the cement, the iron and steel, and the coke and chemical industries in Cairo; text ile, food, oil and soap industries in Alexandria and the Delta reg ion; and sugar in Upper Egypt. During the last few years, significant attention has been given to the protection of the Nile fro m pollution. The focus is on industrial establishments, since industrial wastewater is the major contributor to Nile pollution [12].
Pollution fro m agricu ltural activit ies, the pollutants from unsound agricultural pract ices comprise leached salts, nutrients such as nitrogen and phosphorous, and a wide variety of pesticides. In the Delta, salin ity of drainage water increases because of intensive agriculture. This salinity can have a negative impact on the quality of fresh irrigation water, and hence on soil properties. Since the beginning of the 1990s, the use of fertilizers and pesticides in agriculture has been declining due to the adoption of technologically advanced cultivation practices, together with the availab ility of better quality seeds. During 2000/ 2001, the MSEA announced the River Nile to be free fro m industrial pollution. This significant environ mental improvement is resulting fro m the co mpliance with environ mental laws and regulations of 34 large industrial establishments, previously responsible for discharging a total of 100 millions m3/year of untreated industrial waste to the river. Their co mpliance was ensured due to continuous inspection visits carried out by a committee with representatives from MSEA, the Ministry of Water Resources and Irrigation and Surface Water Police Depart ment. A total of approximately 360 million Egyptian pounds had been invested in pollution abatement at the 34 establishments. Four docking stations for receiving wastes fro m Nile cru ise boats became operational. The stations are located in Cairo, El M inya, Assiut, and Sohag. Another station located in Aswan has been constructed .In conjunction with this, standards and specifications were prepared for the construction of new cruise docking stations along the Nile, as well as the operation of Nile cruise boats. Furthermore, continuous activities are carried out mon itoring the river water quality. In the summer of 2000, a study was published concerning the water quality of the River Nile, carried out by the Central Laboratory of EEAA in collaboration with the Ministry of Water Resources and Irrigation, and covering a stretch of the river fro m south of Helwan to the mouth of the Damietta and Rosetta branches along the Mediterranean. Underway is the finalization of the 2001 study, which covers the whole stretch of the river in Egypt, fro m Aswan to the Mediterranean [12].
Despite its upstream course of mo re than 6,000 KM, the Nile is relatively unpolluted when it reaches Cairo. Cairo dispenses 2-3 million cubic meters of domestic sewage and 0.2 million cubic meters of industrial effluents generated daily. The large flow of water in the Nile (daily flo w of 80-150 million cubic meters) provides extensive dilution of pollutants. After treatment and chlorinated, the drinking water generated fro m the 16 water treat ment p lants in Cairo is nearly always clean. Ho wever, the crisis lies in the distribution system. In so me areas the pressure reaching houses is considered low for consumption, pipe lines are deteriorated in some areas. To co mpensate for the pressure loss, it is not uncommon for houses to be equipped with power pumps and holding tanks. The negative pressure generated increases the likelihood of infiltration of ground water and sewage into the pipes. Moreover, the tanks are open for contamination by atmospheric depositions, birds and animals [13].
In addition, chemical contamination of the drinking water especially in Cairo and other large Egyptian cities are not to be neglected. Chlorine, carcinogenic pesticides, heavy metals especially lead all been detected in varying levels in our drinking water. In Cairo, however, the main health hazard fro m drin king water is lead contamination. Microbiological contamination of drinking water in some suburban areas and rural commun ities in Egypt is not to be neglected. Potent water supplies are still not availab le in a major sector of non-urban areas of Egypt including a large section of Egyptian population [14].

Pesticides Exposure and Reproducti ve Heal th
Organochlorines including endrin, d ieldrin, lindane, and DDT were the most widely used pesticides in Egypt through the early 1980's. The pesticides are dangerous as they are very persistent in the environment and bioaccumulative in fatty tissue. In Egypt, ch lorinated hydrocarbons (DDT) are still in use in some rural agricultural areas. Pesticide food contamination may be a major health threat to the general population in Egypt. Varying amounts of pesticides applied to crops in the field may remain on food surfaces or be incorporated systemically into the plant. Subsequent washing, processing, and cooking may remove some but not all of the pesticide residue. Pesticides may apply to crops after harvest (DDT) to prevent spoilage during transport and storage. Pesticides may even appear in crops to which they were not applied when irrigation water that has been contaminated by upstream pesticide use is re-used for additional crops.

Power Stations
The main regulation for Cairo Electricity Distribution Co mpany has been issued and its fourth element stated that the geographic area for the co mpany's activities includes Cairo, Giza, and the extension of Great Cairo in Kaloubia governorate. Due to the large size of this geographic area and overpopulation in the above mentioned governorates, a huge burden is put on the company in supplying the power services to the citizens in the governorates and in securing the feeding of electricity for d ifferent usages, through a large distribution network covering all cities and villages in the mentioned governorates serving a large number of citizens. This is clear when co mparing the components of this network, the number of customers and the total nu mber of assets and debits, with other distribution companies.
Managing, operating and maintain ing this large network require tremendous efforts and incessant work fro m the group managing the company, in addition to their work in solving the citizens' problems and facilitating the procedures of securing the stability and continuity of electric supply. However more concentration and effort are needed for continuous monitoring and supervision of the co mpany's activities to ensure the continuity and stability of the electric supply. In order to achieve the above mentioned goals, Cairo Electric Distribution Co mpany on 9/11/ 2004 was divided into two companies:

Effects of Industrial Pollution on Health
The environ ment in wh ich we live can be considered as having three fundamental sets of components: Physical (energy of one form or another), Chemical (i.e. substances whether natural or man-made), Bio logical (liv ing things). Hazards can present themselves to us in various media e.g. air, water and soil. The influence they can exert on hu man health is very complex and may be modulated by our genetic make up, psychological factors and by the perceptions of the risks that they present. The cause and development of nearly every human d isease is in so me way related to environ mental factors. Diet and nutrition, infectious agents, toxic chemicals, physical factors and physiological stress all play a ro le in the onset or progress of human diseases [15]. One of the characteristics of chemical o r physical carcinogenesis is the usually extended period of time (latent period) between contact with the carcinogen and the appearance of a tumor. The latent periods of occupational cancers may extend fro m one to several years and commonly to several decades. Initiat ion and promotion are t wo stages in the development of tu mors. In itiation is caused by chemical, physical, or biological agents, which irreversibly and heritable alter the cell genome [16].

Modeling a Spatial Dss
Modern SDSS such a system contains data and analysis functions of GIS, RS, DSS, and models and depends on given expert knowledge. To generate a SDSS with these topics, it is essential to imp lement a Model base Management System (M BMS) as well as a Database Management System (DBM S). By applying modern DSS by using GIS analysis functions (industrial pollut ion maps in Cairo and the d istribution of so me d iseases), MBM S and DBMS to find the relations between some industrial pollution and some d iseases. The modern SDSS can be help for landscape of industrial pollution which may be effect on the human health in Cairo, Egypt.
The spatial decision support system (SDSS) contains three main parts. The first are Geographic Informat ion System (GIS) and Remote Sensing (RS), second is Decision Support System (DSS) Models, th ird are Expert System (ES) and Artificial Intelligence (AI). In order to building three databases geo-database, model database and knowledge database as in figure 3. This spatial decision support system (SDSS) can be used by decision makers for designing landscape in Egypt.

Conclusions
Given the advances in co mputer technology in general and GIS techniques in particu lar, I suggest that SDSS will be an important component of DSS applicat ions in future. This trend will be driven by the relevance of spatial in formation as a component of the informat ion needed for a wide range of decisions. This class of DSS will make an important contribution, not because of its use of the latest technology, but because it will allow decision makers incorporate a spatial dimension in their decision making. This spatial dimension is an important feature of many areas of DSS application. These potential areas of application including fields, such as routing or marketing, wh ich have been important fields of DSS application in the past. For this larger class of decision makers who might use SDSS, spatial data will be used with other types of informat ion required originating in specialised models, often of a non-spatial nature. For this broad range of applications, GIS technology alone can only make a partial contribution to decision support. Comprehensive decision support will require the effective integration of GIS and non GIS techniques. This can be achieved by building systems using a GIS as a DSS generator. The building of SDSS by using GIS has been facilitated by recent technical develop ments, both within GIS software, and in programming tools generally.