GEOSTATISTICAL ANALYST AS A TOOL TO PREDICT NUTRIENTS ... · Thirteenth International Water Technology Conference, IWTC 13 2009, Hurghada, Egypt 1201 GEOSTATISTICAL ANALYST AS A TOOL

Thirteenth International Water Technology Conference, IWTC 13 2009, Hurghada, Egypt

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GEOSTATISTICAL ANALYST AS A TOOL TO PREDICT

NUTRIENTS POLLUTION IN WATER

Gehan A. H. Sallam 1 and Mohamed Embaby 2

1 Researcher, Drainage Research Institute (DRI), National Water Research Centre (NWRC), Delta Barrages (El Kanater), P.O Box 1362/5, Cairo, Egypt

E-mail: [email protected]

2 Research Assistant, National Water Research Center, Fum Ismailia Canal, P.O Box 74, Shoubra El-Kheima 13411, Cairo, Egypt

E-mail: [email protected] ABSTRACT Egypt is one of the countries that facing great challenges due to its limited water resources. However, the Government can enhance water management and sustainable development by adopting policies that enable water demands and supply management. Therefore, there is a need for unconventional methods to provide better tools for the assessment and management of water quality problems. The main objective of this study is to use Geographic Information System (GIS) to in-follow and predict the nutrients pollutants in the water by applying statistical analysis within GIS by using Geostatistical Analyst. The study was conducted through two main phases. The first is the field work to obtain spatial data in sample points taken at different locations in the experimental area that necessary to assess the pollutants and use GIS to store and manipulate this geographic information. The second is applying statistical analysis within GIS by using Geostatistical Analyst to create interpolated surfaces based on the similarity of nearby points to create a continuous surface to help in data management, mapping (visualizing) and analysis. It was validated by removing part of the data and the rest of the data was used to predict the removed part. The comparison is mainly dependant on the Mean Absolute Error (MAE) and the percentage of error (%Error) to asses the best and the worst case. It was found that the MAE for all predicted pollutants was small and the %Error was less than 18 % for all pollutants. So it is clear that the Geostatistical Analyst is validated as a new tool for spatial data exploration, evaluation of error in prediction surface models, statistical estimation, and optimal surface creation. So it is undoubted that application of Geostatistical Analyst as an extension to GIS for spatial data exploration and maps creation adds a very important practical and economical value. Keywords: Geographic Information System (GIS), Geostatistical Analyst, nutrients

pollutants, Mean Absolute Error (MAE), percentage of error (%Error)

Thirteenth International Water Technology Conference, IWTC 13 2009, Hurghada, Egypt 1202

1. INTRODUCTION According to the UNDP report [1] it is clear that in Egypt, the demand for water in agriculture, industry and municipal uses has been increasing due to population growth and increase in aggregate income. The Government can enhance water management and sustainable development to reflect water scarcity by adopting policies that enable water demands management in addition to water supply management. However, there is a need for an unconventional method to provide better tools for the assessment and management of water quality problems to adopt management policies and establish the limits for sustainable water reuse strategy. Nutrients pollutants are environmentally undesirable because they can contribute to pollution of water resources. They are likely presented in the nitrate, ammonia and phosphate forms. Therefore, they are regarded as potential source of environmental pollution, DWIP [2]. Krivoruchko and Gotway [3] stated that, nowadays Geographic Information System (GIS) provides a powerful collection of tools for the management and visualization of spatial data. Khaled Abu-Zeid [4] also mentioned that the GIS is a computer –based system that is used to store and manipulate geographic information. GIS techniques can foster better decision-making by allowing managers to conduct analyses that would be impractical or infeasible otherwise. Under such conditions, it is undoubted that application of GIS is a powerful tool for understanding, modelling, and managing complex water quality problems. Statistical analysis within GIS is rapidly becoming an impressive suite of tools developed for statistical analysis of spatial continuous data. These data can be measured at any location in space. Geostatistical Analyst is one of the statistical software packages through the GIS. It is an extension of Arc Map™, which bridges the gap between geostatistics and GIS and provides a powerful collection of tools for the management and visualization of spatial data. Not only are interpolated surfaces created, but also a wide range of analytical and exploratory tools are incorporated to extract useful information from the data, http://www.esri.com/library/whitepapers/geostat.pdf [5]. It is a flexible software package that allows any user with spatially continuous data to explore and analyze their data using statistical tools and interpolate optimal surfaces. Some of the various fields that utilize Geostatistical Analyst include environmental and agricultural issues, exploration, geology, meteorology, hydrology, archeology, forestry, health care, mining and real estate. Therefore, the main objective of this study is to use Geographic Information System (GIS) to in-follow and predict the nutrients pollutants in the water resources including subsurface water and drainage water, for reuse strategy, by applying statistical analysis within GIS with Geostatistical Analyst. 2. METHODOLOGY

The study was divided into two main parts. The first part is the experimental field work that was formulated to obtain measured data necessary to assess the pollutants in water. The second part is the statistical analysis of the data within the GIS using


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Geostatistical Analyst. The field measurements were carried out in Mashtul Pilot Area (MPA). It is located in the south-eastern part of the Nile Delta with an area of approximately 260 feddans (1 feddan = 0.42 ha). The area is divided into eighteen units. MPA was controlled under the current farming conditions aiming to apply the data measurement program for two years. The area was surveyed with the Global Positioning System (GPS) to locate the boundaries of the area and the sampling locations with the GIS (Figure 1). The measurement program included collecting water samples including subsurface water and drainage water periodically every 10 days. The samples were analyzed in the laboratory to determine the nutrients including the three parameters of nitrate (NO3), ammonia (NH4) and total phosphate (PO4) for samples to follow the pollution migration. Then, Geostatistical Analyst was used to analyze the data that was measured in the field. The Geostatistical Analyst was also validated as a new tool for spatial data exploration, evaluation of error in prediction surface models, statistical estimation, and optimal surface creation.

Figure 1. Mashtul Pilot Area Layout and Sampling Locations 3. RESULTS AND DISCUSSION

MPA was controlled for two years, from June 2005 to May 2007, to apply the field experimental measurements program. The area was controlled under the current farming conditions to apply the measurements program to in-follow the nutrients pollutants. The measurements program included collecting water samples periodically every 10 days. The subsurface water samples were collected from the observation wells in the eighteen units of the area. The drainage water samples were collected from


manholes for field lateral drainage water sampling, collectors' out-lets for collector drainage water sampling and open drains for the main drainage water sampling before and after collector out-let. The samples were analyzed to determine the nutrients concentrations to follow the pollution migration. The results of laboratory analysis for drainage water samples of summer season including subsurface water, drainage water from collector and drainage water from the main open drain before and after the out-let of the collector of the subsurface agricultural drainage are shown in Tables 1, 2 and 3.

Table 1. Average Nitrate (NO3) Concentrations in Water Samples for Summer Season

Date Subsurface Water NO3 (mg/l)

DWBC NO3 (mg/l)

Collector NO3 (mg/l)

DWAC NO3 (mg/l)

13 6.39 12.35 6.47 6.47 21 6.03 5.88 6.17 3.82 31 5.58 13.97 14.26 5.44 48 5.36 12.22 6.34 5.88 52 5.61 5.96 6.47 5.92 59 3.98 9.16 8.69 4.24 65 6.02 8.95 6.72 7.06 75 21.04 10.88 15.50 5.42 80 14.10 14.03 12.08 8.48

DWBC: Drainage Water before Collector outlet DWAC: Drainage Water after Collector outlet Table 2. Average Ammonia (NH4) Concentrations in Water samples for Summer Season

Date Subsurface Water NH4 (mg/l)

DWBC NH4 (mg/l)

Collector NH4 (mg/l)

DWAC NH4 (mg/l)

13 1.45 1.92 1.15 7.09 21 1.63 4.42 2.95 22.37 31 1.60 9.46 7.00 13.82 48 1.16 2.04 4.83 10.43 52 1.19 2.28 5.93 4.07 59 1.35 9.66 8.25 8.47 65 1.37 3.00 2.75 3.37 75 1.47 1.18 9.50 12.36 80 1.10 3.80 3.32 3.35

DWBC: Drainage Water before Collector outlet DWAC: Drainage Water after Collector outlet


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Table 3. Average Phosphate (PO4) Concentrations in Water Samples for Summer Season

Date Subsurface Water PO4 (mg/l)

DWBC PO4 (mg/l)

Collector PO4 (mg/l)

DWAC PO4 (mg/l)

13 0.32 11.72 12.86 12.47 21 0.31 14.21 13.69 17.66 31 1.11 9.14 8.96 7.16 48 0.24 42.73 16.02 11.83 52 0.54 6.37 6.29 5.98 59 0.74 16.09 13.86 17.67 65 0.43 6.36 6.02 6.81 75 1.30 14.79 28.17 21.18 80 0.87 6.62 10.32 6.16

DWBC: Drainage Water before Collector outlet DWAC: Drainage Water after Collector outlet Tables 4, 5, and 6 show the results of laboratory analysis for water samples of winter season including field lateral drainage of Berseem and Wheat fields, drainage water from collector and drainage water from the main open drain before and after the out-let of the collector of the subsurface agricultural drainage for nitrate (NO3), ammonia (NH4), and phosphate (PO4) parameters, respectively.

Table 4. Average Nitrate (NO3) Concentrations in Water Samples for Winter Season

Date Subsurface Water NO3 (mg/l)

DWBC NO3 (mg/l)

Collector NO3 (mg/l)

DWAC NO3 (mg/l)

13 3.79 5.52 3.07 3.76 26 3.28 10.58 2.60 7.52 33 7.21 5.63 5.63 4.70 41 6.12 6.47 55.02 52.92 51 4.13 16.21 19.74 7.77 56 4.28 9.41 15.88 15.29 64 3.83 12.05 6.17 5.59 70 5.35 6.76 17.93 13.82 81 5.99 14.11 10.00 7.64

DWBC: Drainage Water before Collector outlet DWAC: Drainage Water after Collector outlet Table 5. Average Ammonia (NH4) Concentrations in Water Samples for Winter Season

Date Subsurface Water NH4 (mg/l)

DWBC NH4 (mg/l)

Collector NH4 (mg/l)

DWAC NH4 (mg/l)

13 5.64 1.18 1.08 4.98 26 2.84 1.05 0.73 0.85 33 2.85 1.24 1.44 1.38 41 2.85 28.67 43.41 54.87 51 3.51 0.85 1.51 1.38 56 5.31 1.23 1.72 1.31 64 3.82 1.72 1.47 0.98 70 3.46 1.15 1.72 1.39 81 3.70 1.15 0.98 1.31



Table 6. Average Phosphate (PO4) Concentrations in Water Samples for Winter Season

Date Subsurface Water PO4 (mg/l)

DWBC PO4 (mg/l)

Collector PO4 (mg/l)

DWAC PO4 (mg/l)

13 5.20 1.38 1.54 6.79 26 2.48 2.74 0.40 2.55 33 3.63 0.68 0.29 0.65 41 2.54 0.39 0.42 2.73 51 0.98 7.37 6.13 6.33 52 4.12 3.19 3.35 2.20 53 2.45 ---- ---- ---- 56 1.82 1.93 2.31 2.41 64 0.59 1.80 2.36 2.65 65 2.13 ---- ---- ---- 70 2.68 1.98 2.30 2.31 81 0.87 2.74 4.01 3.56 82 2.65 1.59 1.41 92 2.59 3.18 2.58 0.37


3.1 Geostatistical Analyst

In this part, the data was investigated and geostatistically analyzed using geostatistical analyst. The geostatistical analysis of the data was carried out in two main stages. The first stage is exploring data and the second one is creating the maps. Figure 2 and Figure 3 show the nitrate (NO3) concentrations in drainage water and subsurface water respectively as an example of the predicted creating maps by using geostatistical analyst within GIS.


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Figure 2. Nitrate Concentration Maps of Drainage Water


Figure 3. Nitrate Concentration Maps of Subsurface Water


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3.2 Geostatistical Analyst Validations

The validation process of Geostatistical Analyst was conducted by removing part of the data and the rest of the data was used to predict the removed part of the data� The first group consists of 75% of the measured data and used to plot the distribution maps of the elements. The second group, 25% of the data, which was predicted by the model, was compared with their field measured data to validate the model as a prediction tool. The process was conducted by using the main sets of the observed data for the six measured elements (Ammonia, Nitrate, total Phosphate, Salinity (EC), Chloride (CL), and Potassium (K)). The comparison is mainly dependant on the percentage of error (%Error) and the mean absolute error (MAE) to asses the best and the worst case as shown in Figure 4 and Figure 5 for drainage water and subsurface water respectively.

Drainage Water

0.01

0.10

1.00

10.00

100.00

Measured Value Predicted Value MAE

Val

ue

(ppm

)

NH4

NO3

PO4

EC

CL

K

Figure 4. Measured, Predicted Values & MAE of Studied Elements in Drainage Water

Subsurface Water

0.01

0.10

1.00

10.00

Measured Value Predicted Value MAE

Val

ue (p

pm)

NH4

NO3

PO4

EC

CL

K

Figure 5. Measured, Predicted Values & MAE of Studied Elements in Subsurface Water


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Table 7 shows the overall validation parameters for prediction of the six measured elements in the drainage water and subsurface water. It was found that the mean values for measured and predicted data for all elements are close to each other. The difference between them is near to zero for NH4, PO4, EC and K, and less than 0.5 for NO3 and CL. The MAE for all cases is small and the %Error is less than 18% for all elements.

Table 7. Qualifying Parameters for Prediction of Data

Data Observed Data Predicted ALL Elements

Mean Mean MAE % Error NH4 2.69 2.72 0.44 0.16 NO3 11.83 12.32 2.04 0.17 PO4 2.85 2.76 0.50 0.18 EC 1.80 1.83 0.25 0.14 CL 8.23 8.05 0.99 0.12

K 0.66 0.66 0.05 0.07 4. CONCLUSIONS

The main objective of this study was to validate Geostatistical Analyst as a new tool for predicting nutrients pollution in water. The Geostatistical Analyst was also validated as a new tool for spatial data exploration, evaluation of error in prediction surface models, statistical estimation, and optimal surface creation. The validation process was conducted by using the main sets of the observed data for the six measured elements. The comparison was mainly dependent on the percentage of error (%Error) and the mean absolute error (MAE) to asses the best and the worst case. It was found that the mean values for measured and predicted data for all elements are close to each other. The MAE for all cases was small and the %Error was less than 18 % for all elements. So it is undoubted that application of Geostatistical Analyst as an extension of GIS for spatial data exploration, data prediction and maps creation adds very important practical and economical value which results of reducing the number of observed samples. 5. RECOMMENDATIONS

1- It is recommended to generalize the application of geostatistical analyst within the GIS in the Ministry of Water Resources and Irrigation (MWRI) as an effective tool to follow the nutrients pollution in water, to assess and to manage the water quality problems in order to provide sufficient data to help decision maker to adopt the adequate management policies and establish the limits for sustainable drainage water reuse.


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2- It could also recommend applying geostatistical analyst within the GIS to follow the other elements of pollution as a practical and economical effective tool of spatial data exploration and maps creation using geostatistical interpolation technique that is based on statistics.

REFERENCES

1. UNDP, 2002, "The Arab Human Development Report". Creating Opportunities for Future Generations. Regional Bureau for Arab States, New York.

2. DWIP (Drainage water Irrigation Project), 1997, "Final Report", Drainage

Research Institute, Cairo, Egypt. 3. Krivoruchko, K. and Gotway, C. A., 2003, "Using Spatial Statistics in GIS".

Proceedings of the International Congress on Modeling and Simulation, Townsville, Australia.

4. Khaled, M. Abu-Zeid, 1994, "A GIS Multi Criteria Expert Decision Support

System for Water Resource Management", Ph.D. Thesis, Colorado University, USA.

5. http://www.esri.com/library/whitepapers/Kriging_in_Geostatistical_Analyst.

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GEOSTATISTICAL ANALYST AS A TOOL TO PREDICT NUTRIENTS ... · Thirteenth International Water Technology Conference, IWTC 13 2009, Hurghada, Egypt 1201 GEOSTATISTICAL ANALYST AS A TOOL