“Adjustment of DRASTIC Vulnerability Index to Assess Vulnerability for Pollution Using the Advection Diffusion Cell”

Von der Fakultät für Georessourcen und Materialtechnik der Rheinisch-Westfälischen Technischen Hochschule Aachen

zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften

genehmigte Dissertation vorgelegt von M.Sc.

Taiseer Harb Aljazzar

aus Khanyounis, Gazastreifen

Berichter: Univ.-Prof. Dr.rer.nat. Dr. H.c. (USST) Rafig Azzam Univ.-Prof. Dr rer.nat. Thomas R. Rüde

Tag der mündlichen Prüfung: 16. Juni 2010

Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfügbar

Taiseer Harb Aljazzar Adjustment of DRASTIC Vulnerability Index to Assess Groundwater Vulnerability for Nitrate Pollution Using the Advection Diffusion Cell

ISBN: 3-86130-621-2 1. Auflage 2010

Bibliografische Information der Deutschen Bibliothek Die Deutsche Bibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Da- ten sind im Internet über http://dnb.ddb.de abrufbar.

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D 82 (Diss. RWTH Aachen University, 2010)

Gedruckt mit Unterstützung des des Deutschen Akademischen Austauschdienstes ABSTRACT

This study aims to estimate aquifer vulnerability by applying the standard DRASTIC index as well as utilizing the Emission-Transmission-Immission (ETI) concept to assess the groundwater vulnerability to groundwater contamination by nitrate in a part of the Block (German name: Venloer Scholle) in the Northwestern of North Rhine-Westphalia in Germany. Data for land use, soil, and hydrogeological settings in the study area were collected from different sources such as: The Geological Survey (GD), Ministry for the Environment, Conservation, Agriculture and Consumer Protection in North Rhine-Westphalia (MUNLV), and the German Working Group on Water Issues of the Federal States (LAWA). These data have been used to build the seven parameters of the standard DRASTIC vulnerability index. The final groundwater vulnerability map was constructed. It is clear that more than 65% of the study area can be classified to have high to very high groundwater vulnerability. Geostatistical and sensitivity analysis of the standard DRASTIC index showed that the groundwater recharge and the impact of the vadose zone have the greatest influence on the groundwater vulnerability. Analysis of the effective weights of the DRASTIC parameters indicated that the groundwater recharge and the protective layer represented with the impact of the vadose zone are also the most effective parameter in the final DRASTIC vulnerability index which coincides with the results of the map removal sensitivity analysis. An additional objective is to demonstrate the combined use of the DRASTIC index and the ETI model as an effective method for groundwater pollution risk assessment. The ETI concept was utilized to assess nitrate retardation in different soils using the Advection-Diffusion (AD) cell. The convective-dispersive solute transport equation has been solved analytically to calibrate the results of the Advection-Diffusion cell, and high agreement has been shown. The amount of nitrate sorbed in soil was depending on the grain size distribution and on the organic matter content and was ranging from about 10% in sandy soils to about 60% in clayey soils. The retardation parameter of nitrate in soil was also estimated based on the Advection- Diffusion results which were validated by the analytical solution of the convective-dispersive solute transport equation. The highest retardation parameter occurred in the soil sample with the highest silt and clay portions and the highest organic matter content. The nitrate retardation parameter was integrated in the DRASTIC index and adjusted vulnerability maps (DRAS’TIC, DRASTIC-ETI,

DRASTIC-Rt indices) were produced considering nitrate retardation in soils. It was clear that integrating the retardation factor in DRASTIC index has reduced vulnerability values in the study area, these findings were enhanced by the positive strong correlation between the new modified DRASTIC indices values and nitrate concentrations in groundwater, land use classes and the direction of groundwater flow. The study concluded that (1) the standard DRASTIC index has overestimated the groundwater vulnerability to nitrate pollution, and (2) using the Advection-Diffusion cell based on the Emission-Transmission-Immission concept produced more reliable vulnerability maps after considering nitrate sorption and retardation in soil.

I KURZFASSUNG

Grundwasser ist eine sehr wichtige Quelle der Wasserversorgung in vielen Ländern; in Deutschland entstammen etwa 70% des für die Wasserversorgung benötigten Wassers aus Grundwasservorkommen. Um Gewässer über Staats- und Ländergrenzen hinweg zu schützen, trat im Jahr 2000 die EU-Wasserrahmenrichtlinie (EU-WRRL) in Kraft. Sie beinhaltet das Prinzip des integrierten Gewässerschutzes, d.h. Schutz von Grundwasser, Oberflächengewässern und aquatischen Lebensgemeinschaften. In Bezug auf Grundwasser soll ein guter mengenmäßiger und chemischer Zustand erreicht werden. Nach der Nitrat- Richtlinie der EU-WRRL und im Hinblick auf den Grundwasserschutz sind die Mitgliedstaaten der EU-WRRL verpflichtet, die Verschmutzung und Verringerung des Grundwassers zu verhindern. Wichtige Voraussetzungen, um die Pläne der EU-WRRL einhalten zu können, sind die Abschätzung des Grundwasserschutzpotenzials, der Grundwassergefährdungen und der Grundwasservulnerabilität. In dieser Arbeit wurde die Gefährdung (Vulnerabilität) des Grundwassers im Bereich der nördlichen Venloer Scholle durch Nitrat bewertet. Die Bewertung wurde nach der Anwendung des DRASTIC-Konzepts mit der Berücksichtigung der Retardation von Nitrat im Boden durchgeführt. Das intrinsische DRASTIC-Konzept wurde mit Hilfe des ETI-Konzepts (Emission, Transmission, Immission) und der Advektions-Diffusions-Zelle modifiziert. Dabei erfolgte die Durchführung von Nitrat- Transportversuchen mit dem Ziel, die Transportvorgänge des Sickerwassers ausgehend von Nitrat über den natürlichen Boden in das Grundwasser zu simulieren (Transmission). Dazu wurde im Bereich der nördlichen Venloer Scholle, der Niederrheinischen Bucht, ein rund 100 km² großes Arbeitsgebiet gewählt, in dem eine Reihe bodentypspezifischer und flächennutzungsabhängiger Bodenproben gewonnen und relevante Bodeneigenschaften analysiert wurden. Darüber hinaus wurden unterschiedliche Bodentypen der Venloer Scholle in die Advektions-Diffusions-Zelle eingetragen und der spezifische Retardationsfaktor von Nitrat für diese Bodentypen gemessen und gemittelt, so dass der modifizierte Parameter berechnet werden konnte. Geringe bis schwache Korrelationen zwischen beiden Parametern konnten gezeigt werden. Auch wurde bewiesen, dass die beiden Parameter Boden und die Lithologie der ungesättigten Zone den größten Einfluss auf die DRASTIC-Werte haben und deswegen sowohl der Nitrattransport im Boden, wie auch in der ungesättigten Zone, berücksichtigt werden muss. Die Retardation des Nitrats im Boden wurde durch die Anwendung des ETI-Konzeptes und der Advektions-Diffusions-Zelle bewertet. Die Validierung der Advektions-Diffusions-Zelle erfolgte durch die analytische Lösung der Advektion-Dispersions-Gleichung. Dieser modifizierte Parameter wurde anschließend in die ursprüngliche DRASTIC-Gleichung eingebaut. Die Regionalisierung der Ergebnisdaten wurde mit Hilfe von Geografischen Informationssystemen (GIS) durchgeführt.

II ACKNOWLEDGMENT

Personally, I would like to thank Prof. Dr. Dr. h. c. Rafig Azzam, the head of the Department of Engineering Geology and Hydrogeology for serving as my major supervisor and helping me through my PhD research. I truly appreciated all the time and advice you gave me throughout my time as a PhD candidate. Finally, thanks for treating me with respect and being a friend throughout my doctoral study.

I would like to thank Prof. Dr. rer. nat. Thomas Rüde, the head of the Hydrogeology Scientific Group for being my second supervisor. I appreciated your guidance, support and willingness to take time to discuss my research. Again, thank you for treating me with respect and timely advice throughout my PhD research.

I would also like to thank the DAAD (German Academic Exchange Service) for their financial support for me throughout the time period of my German Language course, my doctoral study at the RWTH Aachen University and dissertation printing costs.

Many thanks go to Mr. Bernd Meyer (Technical Manager of the Geotechnical Laboratory) and Mr. Albert Pitz (Technical Manager of the Hydrogeological Laboratory) for their guidance and help through my work in the laboratories. My thanks go also to Mrs. Birgit Müller for her technical support through my early and advanced stage with dealing with the ArcGIS 9.3. I also express many thanks to the whole staff and my colleagues at the Department of Engineering Geology and Hydrogeology for the warm and friendly atmosphere.

I am thankful for Dr. Mustafa Al-Kuisi (Department of Geology, University of Jordan) for giving me the time to read my thesis and discuss all the important scientific points, thank you for your support. Thank you also to Dr. Mohammad Al-Qinna (Department of Land Management and Environment, Hashemite University, Jordan) for his scientific hints. I also appreciate the time of Dr. Al-Kuisi, Dr. Al-Qinna and Eng. Adel Aljazzar (KfW-Local Coordinator in Gaza Strip) for editing the my thesis.

Finally, I would like to thank my mother for her encouragement and sacrifice, I am thankful for my sisters and brothers for their support and patience. I am also thankful for my father who passed away before seeing the output of our efforts. He was a rich source of motivation and support. Thanks to my wonderful wife, Safaa for supporting me, urging me on, and being patience. Thanks also for my daughters Yumna and Mayaar for being a source of happiness and smile when I was in need for this.

III DEDICATION

To the memorial of the man whom I like most and respect; my father

To the woman whom I appreciate and love, my mother

To my lovely wife Safaa and my dutiful daughters Yumna & Mayaar for their support and patience

To the memorial of the Children of Gaza Strip who passed away and to the Children who are still surviving to have a bright and promising future

IV

LIST OF CONTENTS

ABSTRACT I KURZFASSUNG II ACKNOWLEDGMENT IV DEDICATION V List of Contents ………………………………………………………………………………………… VI List of Figures …………………………………………………………………………………………. IX List of Tables ………………………………………………………………………………………….. XI List of Abbreviations …………………………………………………………………………………. XII List of Symbols ……………………………………………………………………………………….. XIII

1 INTRODUCTION 1 1.1 Introduction ………………………………………………………………………………...... 1 1.2 Problem Description ……………………………………………………………………………. 2 1.3 Motivations ………………………………………………………………………………………. 2 1.4 Objectives ……………………………………………………………………………………….. 3 1.5 Methodology …………………………………………………………………………………….. 4 1.5.1 Assessment of Groundwater Vulnerability ……………………………………………… 4 1.5.2 Assessment of Nitrate Retardation in Soils ………………………………………...... 5 1.5.3 Coupling of DRASTIC and the Retardation …………………………………………….. 5 1.6 Thesis Organization ……………………………………………………………………………. 5

2 STUDY AREA DESCRIPTION 7 2.1 Regional Layout ………………………………………………………………………………… 7 2.2 Climate and Hydrological Features …………………………………………………………….. 7 2.2.1 Morphology ………………………………………………………………………………… 7 2.2.2 Land Use …………………………………………………………………………………… 8 2.2.3 Climatology ………………………………………………………………………………… 9 2.2.4 Surface Waters ……………………………………………………………………………. 9 2.3 Geological Settings …………………………………………………………………………….. 13 2.4 Hydrogeological Settings ………………………………………………………………………. 15 2.5 Groundwater: Aquifer Systems ……………………………………………………………….. 18 2.6 Groundwater Abstraction and Nitrate Pollution ……………………………………………… 21

V

3 THEORY & LITERATURE REVIEW 24 3.1 Introduction ……………………………………………………………………………………… 24 3.2 Groundwater Vulnerability ……………………………………………………………………... 26 3.2.1 Concept of Groundwater Vulnerability ………………………………………………….. 26 3.2.2 The Purpose of Vulnerability Assessment ……………………………………………… 27 3.2.3 Vulnerability Assessment Methods ……………………………………………………… 28 3.3 Groundwater Vulnerability Using DRASTIC ………………………………………………… 32 3.3.1 Direct Use of DRASTIC ………………………………………………………………….. 32 3.3.2 Specific Applications of DRASTIC and Other Indices ………………………………… 33 3.3.3 Comparison of DRASTIC and Other Indices …………………………………………… 35 3.3.4 Sensitivity Analysis of Vulnerability Indices ……………………………………………. 37 3.3.5 GIS and Vulnerability Assessment ……………………………………………………… 37 3.4 Groundwater Nitrate Pollution ………………………………………………………………… 39 3.4.1 Nitrate: Health and Environmental Impacts …………………………………………….. 39 3.4.2 Origins of Nitrate in Groundwater …………………………………………………….…. 41 3.4.3 Nitrogen Cycle and Nitrate Processes in Subsurface …………………………………. 45 3.4.5 Nitrate Denitrification in Soil and Groundwater ………………………………………… 49 3.4.6 Nitrate Sorption and Retardation in Groundwater ……………………………………... 51 3.4.7 Nitrate Retardation and ……………………………………………………….. 54

4 DRASTIC: APPROACH & RESULTS 56 4.1 Introduction ……………………………………………………………………………………… 56 4.2 Description of DRASTIC Index ………………………………………………………………... 57 4.3 Groundwater Vulnerability Assessment in the Study Area ………………………………… 61 4.3.1 Data Collection ……………………………………………………………………………. 61 4.3.2 Description and Evaluation of DRASTIC Parameters ………………………………… 61 4.3.3 Evaluation of DRASTIC Vulnerability Index ……………………………………………. 70 4.3.4 Sensitivity Analysis of the DRASTIC Index …………………………………………….. 73 4.3.5 DRASTIC Vulnerability: Nitrate and Land Use ………………………………………… 75

5 ETI: APPROACH & RESULTS 83 5.1 Emission-Transmission-Immission (ETI) Concept ………………………………………….. 83 5.2 Transmission Estimation Using the Advection-Diffusion Cell ……………………………… 85 5.2.1 Experimental Set-Up ……………………………………………………………………… 85

VI

5.2.2 Testing Procedure ………………………………………………………………………… 88 5.2.3 Collection of Soil Samples ...……………………………………………………………... 89 5.2.4 Samples Preparation and Installation in the Advection-Diffusion Cell ………………. 89 5.2.5 Evaluation Concept of the Advection-Diffusion Cell Results …………………………. 95 5.3 Results of the Advection-Diffusion Cells ……………………………………………………... 99 5.3.1 Chemical Parameters …………………………………………………………………….. 99 5.3.2 Mass-Balance and Nitrate Retardation for the Coarse-Texture Soils ……………….. 99 5.3.2 Mass-Balance and Nitrate Retardation for the Fine-Texture Soils …………………... 105 5.3.3 Analytical Solution and the Advection-Diffusion Cell ………………………………….. 107

6 DRASTIC & ETI COUPLING 112 6.1 Introduction ……………………………………………………………………………………… 112 6.2 Conceptual Background ………………………………………………………………………. 112

6.2.1 Modifying the Soil Parameter (S) with the Recovery Factor (Rrc) ……………………. 113

6.2.2 Subtracting the Recovery Factor (Rrc) as New and Single Parameter ……………… 113

6.2.3 Subtracting the Retardation Parameter (Rt) ……………………………………………. 113 6.3 Adjusted DRASTIC Vulnerability Indices ………………………………………………...... 114 6.4 Sensitivity Analysis of the New DRASTIC Indices ………………………………..………….. 124

7 SUMMARY & RECOMMENDATIONS 125 7.1 Summary ………………………………………………………………………………………… 125 7.2 Recommendations ……………………………………………………………………………… 127

8 REFERENCES 129

VII LIST OF FIGURES

1.1 A schematic view of the main methodology approaches in the PhD thesis 4 ………...... 1.2 Outline of the thesis: Chapters and Content 6 …………………………………………………. Location of the study area and the Venlo Block in the Lower-Rhine Basin as well as in 2.1 8 the North Rhine-Westphalia (NRW) 2.2 Land use categories in the study area and NRW (NRW-MUNLV, 2008) 9 The study area is located in the Catchment and is a sub-catchment of the Maas 2.3 10 (NRW-MUNLV, 2008)

The study area location in the surface water catchments in the Niers as a part of the 2.4 11 Maas catchment (NRW-MUNLV, 2005b)

The estimated applied N- (annual average value 2001-2005) in the surface 2.5 12 water bodies with the Model MONERIS (NRW-MUNLV, 2005b) 2.6 Initial characteristics of Germany’s surface water bodies (EEA, 1999) 12 The major structural elements and blocks in the Lower Rhine Basin (Schäfer et al., 2.7 13 1996)

The geological layout of the study area in the Venlo Block within the Lower Rhine 2.8 14 Basin (Shäfer and Siehl, 1999)

The three major geological units found in the NRW (Climate & Environment Consulting 2.9 16 Potsdam GmbH, 2006)(Climate & Environment Consulting Potsdam GmbH, 2006) 2.10 Lower Rhine Basin with Pleistocene terraces (Boenigk, 2002) 17 2.11 Stratigraphy and terraces order in the Lower Rhine Basin (Boenigk, 2002) 18 Stratigraphical view of the study area and the Venlo Block (modified after 2.12 19 Klostermann, 2005)

Stratigraphical view of the study area and the Venlo Block (modified after 2.13 20 Klostermann, 2005)

Characterization of groundwater in Germany in terms of quantity and quality according 2.14 21 to the Water Framework Directive WFD (BMU, 2005)

Frequency distribution of nitrate concentrations measured at the stations of the 2.15 22 Federal Nitrate Groundwater Monitoring Network (Wolter et al., 2000) (a: above) Groundwater quality in terms of chemical pollution including nitrate (>50 mg/L); and (b: below) Groundwater quality in terms of nitrate where the study area 2.16 23 also shows nitrate concentration in groundwater exceeding the allowable limit (MUNLV, 2008) Characterization of the German surface water bodies and groundwater aquifers (BMU, 3.1 25 2006)

3.2 Nitrogen cycle 46

4.1 The main methodology used in this study; DRASTIC and the Advection-Diffusion Cell 56 4.2 Flowchart of groundwater vulnerability assessment based on DRASTIC method 58 Ranges, ratings and weight value of the DRASTIC parameters (modified after: Aller et 4.3 60 al., 1987) 4.4 Distribution of recharge rates over the different land use categories 63

VIII 4.5 Distribution of recharge rates over the different soil classes 63 4.6 A texture-based soil map of the study area (modified after NRW-GD, 2002) 66 Maps of the different ratings of the DRASTIC parameters (a) Depth to groundwater 4.7 67 table, (b) Recharge rates, (c) Aquifer media, and (d) Soil media

(continue): Maps of the different ratings of the DRASTIC parameters (e) Topography, 4.7con. 68 (f) Impact of vadose zone and (g) hydraulic Conductivity 4.8 Distribution of DRASTIC parameters, their sub-classes, and their rating values 69 4.9 Methodological approach of the DRASTIC vulnerability assessment 70 The final Groundwater DRASTIC-Vulnerability Map (Based on the standard DRASTIC 4.10 71 index) 4.11 Statistical overview of the DRASTIC index over the entire study 72 4.12 Statistical overview of the DRASTIC vulnerability index over the entire study area 73 4.13 Map removal sensitivity analysis for the DRASTIC parameters 75 4.14 Effective weight sensitivity analysis of DRASTIC parameters 75 Normal distribution of nitrate concentration in the upper unconfined aquifer for three 4.15 77 different periods 4.16 Nitrate concentration in groundwater (average value for the years from 2000 to 2006) 78 4.17 Kriging variance of 142 sampled points of nitrate concentration 79 4.18 Nitrate distribution over the different DRASTIC classes 80 4.19 Nitrate distribution over the different land use categories in the study area 80 Distribution of the final DRASTIC vulnerability index over the different land use 4.20 81 categories in the study area

The main concept of the ETI and the Advection-Diffusion cell to assess nitrate 5.1 84 retardation

Schematic diagram of the Advection-Diffusion Cell. [1] The balancing reservoirs for nitrate, [2] the balancing reservoirs for distilled water, [3] water pump, [4] the cell with 5.2 86 the soil sample, [5] the collecting reservoir for nitrate, and [6] the collecting reservoir for water (after Azzam and Lambarki, 2004; Lambarki, 2006)

The main parts of the soil cell which is the main part of the Advection-Diffusion cell 5.3 87 used to assess transport parameters of pollutants in soils 5.4 Locations of the soil samples within the different soil classes in the study area 90 5.5 Soil sampling in different locations in the study area 91 5.6 Preparation of the soil sample in the Advection-Diffusion Cell 92 The principle of the breakthrough related to the Advection-Diffusion cell (After Hamad, 5.7 97 2003) 5.8 Evaluation concept of the Advection-Diffusion Cell (After Lambarki, 2006) 98 5.9 The typical trend of the Advection-Diffusion results for the coarse-texture soils 100 Nitrate fluxes (the input and the output fluxes with initial nitrate concentration of 75 5.10 mg/L) for the Advection-Diffusion cell of course-texture soil samples and 102 corresponding recovery factors (the area confined between the two curves of nitrate 5.11 A result example of one of the fine-texture soil samples 105 5.12 Thin organic layers on the top surface of the fine-texture soil samples 106

IX The results of the analytical solution of the advective-dispersion equation for the 5.13 109 Advection-Diffusion cell (coarse-texture soil samples)

The results of the analytical solution of the advective-dispersion equation for the 5.14 110 Advection-Diffusion cell (fine-texture soil samples)

Groundwater vulnerability map (DRAS’TIC) after modifying the Soil parameter using 6.1 116 nitrate sorption or recovery factor

Groundwater vulnerability map (DRASTIC-ETI) after subtracting nitrate sorption 6.2 (recovery Rrc) assessed using the Advection-Diffusion Cell from the standard 117 DRASTIC index

Groundwater vulnerability map (DRASTIC-R ) after modifying the Soil parameter 6.3 t 118 through dividing it by the corresponding retardation parameter

The standard DRASTIC vulnerability index versus the three modified DRASTIC 6.4 120 indices The standard DRASTIC vulnerability index versus the modified DRASTIC index 6.5 121 (DRASSTIC) and Nitrate in Groundwater

The standard DRASTIC vulnerability index versus the modified DRASTIC index 6.6 122 (DRASTIC_Rrc) and Nitrate in Groundwater The standard DRASTIC vulnerability index versus the modified DRASTIC index 6.7 123 (DRASTIC_Rt) and Nitrate in Groundwater

X LIST OF TABLES

3.1 Nitrogen forms and their occurrence ……………………………………………………….. 42 3.2 Source and origins of nitrate in soil and groundwater …………………………………….. 44 Description of the hydrogeological parameters used to assess the relative degree of 4.1 59 groundwater vulnerability using DRASTIC (modified after: Aller et al., 1987) ………….

Properties of soil samples collected and tested for the Advection-Diffusion cell (coarse- 5.1 93 texture soils CS) ……………………………………………………………………………….

5.1 Properties of soil samples collected and tested for the Advection-Diffusion cell (fine- 94 con. texture soils FS) (Continue) …………………………………………………………………..

Transport parameters of nitrate in the tested soil samples (coarse-texture) including 5.2 104 retardation parameters according to the results of the Advection-Diffusion cell ……….

Transport parameters of nitrate in the tested soil samples (coarse-texture) including 5.3 108 retardation parameters according to the analytical solution ……………………………… Transport parameters of nitrate in the tested soil samples (fine-texture) including 5.4 retardation parameters according to the analytical solution ……………………………… 108

X 1. INTRODUCTION

1.1 Introduction

Groundwater is an invaluable and vital natural resource for the reliable and economic provision of water supply in the world. It is a major source for water supply in arid and semi arid regions, rural areas, developing countries, several mega-cities, and also for the industrialized territories. Therefore, groundwater is probably the most extracted natural source as it is intensively consumed to meet those different demands including domestic, agricultural and industrial ones. Use of groundwater has significantly increased in recent decades and is currently at a global withdrawal rate of about 700 to 800 km3/year (Zektser and Everet, 2004). Worldwide, groundwater systems are experiencing an increasing threat and risk of pollution from agricultural activities, urbanization and industrial development. The rapid growth of human civilization exerts enormous stresses on the available groundwater resources and induces a real water crisis represented in scarcity and quality deterioration. Nitrate is one of the indicators that reflect groundwater quality and its appropriateness to be safely used. It is probably the most widespread groundwater pollutant, especially in the agricultural areas, and is of great concern as its existence in high concentrations threatens both human health and environment. High concentrations of nitrate in drinking water will cause blue-baby syndromes and its relation to cancer risk is still a point of discussion. The continuous depletion of groundwater and the deterioration of its quality have forced the hydrogeologists as well as the decision makers to focus on implementing management and protection plans in order to sustainably safeguard water resources for the current and the future generations. Assessment of the protection capacity of groundwater aquifers and their vulnerability to pollutants, nitrate as an example, is very important for an effective protection plan.

As a basic requirement for effective management, groundwater systems should be delineated according to their sensitivity to be polluted. The result efforts of groundwater vulnerability assessment will give a fundamental overview of protection degree of a certain groundwater system. Afterward, the more vulnerable zones will receive a more concern as well as faster action plans and measures. Such scenario will save time and costs and will give the possibility to safeguarded groundwater effectively. A more precise and specific assessment of groundwater vulnerability to a certain pollutant requires considering all parameters that control the dynamic of that pollutant in the subsurface system.

1 1.2 Problem Description

The European Water Framework Directive (WFD) requires member states of the (EU) to good ecological and chemical status for all water bodies (including groundwater aquifers) by the year 2015. This is a particular challenge for Germany, where intensive agriculture and severe nitrate pollution occur in aquifer as well as in surface water bodies. Describing the overall water quality condition is difficult due to the spatial variability of multiple contaminants like nitrate, the focus of this study, and the wide range of water quality indicators (chemical, physical and biological) that might be measured. A good solution is then to target these measures within areas vulnerable to nitrate pollution known as Nitrate Vulnerable Zones (NVZs). Nevertheless, many of the previous vulnerability models, such as DRASTIC (Aller et al., 1987), were developed so that the subjectivity of the modeller was an important factor in the outcome of the model. Moreover, these models are restricted to assess the intrinsic groundwater vulnerability. Such vulnerability indices should consider more parameters through which nitrate transport and transformation in the subsurface system can be described. A major parameter that could be added to DRASTIC vulnerability index is nitrate retardation in soil which includes nitrate sorption and reduction. Although there are different techniques, either numerically- or experimentally-based, to assess nitrate retardation in different soils, nitrate retardation should be accurately assessed to represent the field conditions and to be effectively integrated in the DRASTIC vulnerability index. Accurate evaluation of nitrate retardation requires considering the natural field conditions of nitrate transport and allowing enough mixing time between nitrate solution and soil materials as well as avoiding the over-mixing occurs in the batch experiments.

1.3 Motivations

The Advection-Diffusion (AD) experimental setup have the advantage of being able to simulate the natural conditions of nitrate transport and therefore, will be used in this study to assess nitrate retardation in different soils. The Emission-Transmission-Immission (ETI) concept is also capable to use the Advection Diffusion cell results to assess concentrations of a certain pollutant at different depths between land surface and groundwater table (Azzam, 1993; Hamad, 2003; Azzam and Lambarki, 2004; Lambarki, 2006). After considering the parameter of nitrate retardation in the standard DRASTIC index, a new tool will be available to produce a nitrate-specific vulnerability map.

Moreover, about 70% of in natural waters in Germany are derived from agricultural land representing in non-point sources (NPS) pollution (diffused source) from a variety of substances like fertilizers and organic chemicals. Nitrate has been also detected in more

2 than 52% of the German groundwater aquifers at high concentrations. These concentrations have already exceeded the maximum allowable nitrate concentration (MCL) in drinking water (50 mg/L) which has been determined by the World Health Organization (WHO). Thus, maintaining the quality of groundwater is vital for protecting human and environmental health and is an essential prerequisite for providing a good-quality drinking water. This research is an effort in this direction and is funded by the German Academic Exchange Service (DAAD) as an attempt to achieve the basic requirements by the European Union (EU) to describe and reduce nitrate concentrations in soil and groundwater throughout individual European member states.

1.4 Objectives

The main objective of this research is to couple the DRASTIC with the retardation parameter to develop a methodology for the specific assessment of groundwater vulnerability to nitrate or other pollutants. This will be achieved through modifying the standard DRASTIC method using the retardation parameter produced from the Advection-Diffusion cell (AD cell) and the Emission-Transmission-Immission (ETI). The detailed objectives are:

. Estimating groundwater vulnerability to contamination using the standard DRASTIC method. . Examining appropriateness of the standard DRASTIC index to assess groundwater vulnerability to nitrate using actual data of groundwater nitrate contamination. . Using the Advection-Diffusion cell to evaluate the soils capacity to retard nitrate in the subsurface system. . Validating the results of the advection-diffusion cells using the analytical solution of the convective-dispersive solute transport equation. . Understanding parameters that contribute in retarding and reducing nitrate in the subsurface system. . Establishing a new DRASTIC index by joining the standard DRASTIC one with the results of the Advection-Diffusion cell experiments.

This study will provide a help in protecting public water supplies in areas of high or rising nitrate levels in groundwater through developing an objective vulnerability model that includes the most important factors affecting vulnerability of groundwater to nitrate pollution. The developed methodology (tool) will be used to evaluate groundwater vulnerability to nitrate in the Venlo Block (German name: Venloer Scholle) in the North Rhine-Westphalia (NRW) in Germany.

3 1.5 Methodology

The methodology used in this PhD research is a combination of the traditional groundwater vulnerability assessment using the DRASTIC index and a new-developed laboratory setting (the ETI and the AD cell) which enables estimating nitrate transport parameters including nitrate sorption and retardation in different soils. The following approaches were used in this thesis:

1.5.1 Assessment of groundwater vulnerability

Intrinsic groundwater vulnerability in a part of the Venlo Block will be estimated using the DRASTIC index. Afterwards, the reliability of this index will be examined using different statistical approaches; mainly using correlation between the vulnerability index and actual nitrate concentration in groundwater as well as land use classes. Sensitivity analysis of the DRASTIC will be conducted to check the relative significance of DRASTIC parameters.

Fig. 1.1: A schematic view of the main methodology approaches in the PhD thesis

4 1.5.2 Assessment of nitrate retardation in soil

The Advection-diffusion cell, a laboratory set up based on mass-balance concept, will be used to asses transport parameters including capacity of different soils to retard nitrate. This laboratory setup is a simulation of the field conditions in which soils are confined between two fluxes with a difference in nitrate concentration (concentration gradient). The upper nitrate flux is allowed to flow with a constant rate through the prepared soil sample and the nitrate flux flowing out of the soil sample is collected and measured for nitrate concentrations. The experiment will last until the input flux will leave the soil sample without being reduced. The difference between the two fluxes from the start of the experiment to the equilibrium time is a measure of how much nitrate has been stored in the soil sample and is an indicator for nitrate retardation in soil. The retardation factors of nitrate in different soils are used to estimate nitrate immission (nitrate amount that will enter the aquifer after a certain land application). The Advection-Diffusion cell and nitrate sorption as well as retardation will be used to quantify the DRASTIC index in terms of nitrate entering the groundwater system.

1.5.3 Coupling of DRASTIC and the retardation factor

The estimated retardation factors of nitrate in different soils will be integrated in the standard DRASTIC index. This will produce a more-representative method to assess the groundwater vulnerability for nitrate in a certain groundwater basin. The overall described approach will enable us to assess the groundwater vulnerability to nitrate in a more-quantitative method. Such modification will avoid the high DRASTIC subjectivity through considering nitrate transport parameters in different soils.

1.6 Thesis Organization

This PhD thesis consists of seven chapters. The first one describes the problem, the need for this research and the objectives. Chapter two is a description of the study area (Venlo Block) which is a part of North Rhine-Westphalia in the north western part of Germany. The location of the study area and its hydrological features are listed in this chapter. This chapter also includes a full description of the hydrogeological settings and the current nitrate pollution in surface water and the upper aquifer in the study area. A comprehensive literature review is found in chapter three with the theoretical background of the main concepts that appear in this thesis. Besides, chapter three describes nitrate transport and transformation processes in the subsurface system, it mentions also the factors affecting nitrate sorption in soil and the different techniques applied to evaluate this sorption process. Groundwater vulnerability and some attempts for a DRASTIC modification are presented in this chapter. Chapter four

5 outlines the main components of the used approach. It delineates the detailed steps of mapping groundwater vulnerability using the DRASTIC index. A descriptive summary of the Advection-Diffusion cell, the main concept, its components, assembly, and execution is found in chapter five. Chapter five presents also the results obtained and summarizes them in tables, figures, maps and descriptive comments. Moreover, chapter six involves the conceptual background and the results of the coupling between the standard DRASTIC index and the Advection-Diffusion cell results. The last chapter concludes this thesis with a summary of the complete research project. There is also a short summary at the end of each chapter as a trial to keep the readers in a continuous concentration and to give the chance for the short-time reading.

Fig. 1.2: Outline of the thesis: Chapters and Contents

6 2. STUDY AREA DESCRIPTION

2.1 Regional Layout

The study area (/Schwalmtal) is located in the western part of the Federal State of North Rhine-Westphalia (NRW) and along the German border with the . The study area is geologically located in the Lower Rhine Basin and within the Venlo Block and has an area of about 115 km2 (see Figure 2.1). The NRW state is situated in the western- central part of Germany and covers an area of about 34,080 km2 with the highest population density in Germany (about 520 capita/km2) (MUNLV, 2000). It is noteworthy to mention that agricultural practices are a main feature in the study area, and that irrigated areas and rural regions are tightly packed together with the industrial zones (MUNLV, 2008). About 500 km2 are irrigated in North Rhine-Westphalia where most of these kilometres are concentrated in the Lower Rhine Basin. These conditions lead to a situation with high water demands and large amounts of several pollutants especially the increasing nitrate in groundwater (NRW- LANUV, 2000; CEC, 2007). The study area combines the lower half of the topographical map Nettetal (TK-4603) and the upper half of the topographical map Schwalmtal (TK-4703). It extends geographically from the south-western corner (East: 2511200, North: 5680000) to the north-eastern corner (East: 2523500, North: 5690000) according the geo-referencing system of Germany Zone II.

This chapter describes the main features of the study area and indentifies its climatology, land use features, morphology, hydrology and hydrogeological settings. Groundwater flow and nitrate contamination in the study area are also reported in this chapter.

2.2 Climate and Hydrological Features

2.2.1 Morphology

Generally, the Lower Rhine Basin is a flat relief where most of the land surface elevation is within the range 221 m to 451 m above mean sea level (a.m.s.l.). Land surface elevation within the study area decreases smoothly from the North East parts to both the southern and the western directions (Schäfer et al., 2005). Most of the area has a relatively smooth surface elevation of about 30 m a.m.s.l. in the area of Hinsbecker fractures. The hills are a strip extending from southern east to northern west of the study area and have an elevation of about 84 m a.m.s.l. The land surface level decrease again to the east of the Viersen. The whole area is covered with Quaternary sediments with appearance of some valleys (Schäfer et al., 2005).

7

Fig. 2.1: Location of the study area and the Venlo Block in the Lower-Rhine Basin as well as in North Rhine-Westphalia (NRW).

2.2.2 Land Use

The main feature of the study area as well as the whole NRW is the agricultural activities which occupy about 50% of the study area (see Figure 2.4). These activities are of great importance because agricultural practices are always sources of different groundwater pollutants especially nitrate and pesticides. However, the study area is also intensively urbanized: Nettetal; Schwalmtal, Krefeld. Mönchengladbach and Venlo are major cities within

8 and around the study area. Figure 2.2 illustrates the distribution of land use in the study area. However, the urban areas are also considered as a potential source of different pollutants.

Fig. 2.2: Land use categories in the study area and NRW (MUNLV, 2008)

2.2.3 Climatology

The study area is located in a temperate climate zone, where the weather changes frequently. The weather is oceanic and is affected by the west wind zone. The most important climate factors are air temperature and rainfall rates. The hydrological year is characterized by a mild winter and moderate warm summer with uniformly distributed annual rainfall. The average yearly rainfall ranges between 700 and 1200 mm and the average yearly temperature is about 9oC. The temperature ranges from 2oC in January (winter) to about 18oC in July (summer) with a varying monthly rainfall between 45 to 80 mm. The annual precipitation increases from the lee of the Eifel mountains (Euskirchen: 550 to 600 mm) to the west (Jülich: 600 to 650 mm), to the north (700 to 750 mm) and to the east (Cologne|Bonn: 700 to 750 mm) (Gerlach et al., 2006).

2.2.4 Surface Waters

The NRW State is a region with valuable amounts of surface water where water bodies (about 50,000 km length) are distributed over the State. As shown in Figures 2.3 and 2.4, the study area is completely located in the German part of the Maas catchment and exactly within the Niers| catchments. Maas River originates in the Belgian-French mountains and discharges in the with an average flow of about 260 m3/s where as the maximum annual flow of about 2800 m3/s(Van den Berg, et al., 1996). The River

9 runs through the study area with a length of about 28.2 km and has a catchment area of about 165 km2. The average flow of the River Nette is about 0.81 m3/s. The Nette River discharges in the Niers River with some other lakes (about twelve small lakes) within the catchment of the Nette (MUNLV, 2005a).

Fig. 2.3: The study area is located in the Niers Catchment and is a sub-catchment of the Maas River (MUNLV, 2008).

There are several potential forms of threats to groundwater aquifers that could be found in the study area. These forms of deterioration that will adversely affect water bodies in the study area are: water abstraction, morphological alteration and different forms of pollutants. Germany has approximately 400,000 farms that cultivate an average of approximately 0.40 km2 each. This means that approximately 170,000 km2 are used for agricultural purposes, which are 50% of the country’s total surface area. Diffuse inputs (non-point sources) play a major role rather than other pollution forms. Figure 2.5 shows that more than 5 kg/km.yr of fertilizers are applied in the study area whereas Figure 2.6 illustrates that more than 75% of water bodies in the NRW are classified to be polluted or at pollution risk (EEA, 1999).

10

Fig. 2.4: The study area location in the surface water catchments in the Niers as a part of the Maas catchment (MUNLV, 2005b).

The main two examples are nitrate and pesticides. Nitrate accounted in Germany from 1998- 2000 for approximately 80% of all inputs (BMU, 2005) and is mainly generated through the intensive use of different forms of organic and inorganic fertilizers. Surface water in the study area has total nitrogen concentrations exceeding 6 mg/L which is a source of (MUNLV, 2008). Nitrogen loads of surface waters in Niers catchment are substantially larger due to agricultural activities. However, nitrate is also generated through the improper wastewater emissions. There is a strong hydraulic interaction between surface water and groundwater in the study area which requires more attention and monitoring of water quality and especially of the total nitrogen concentration

11

Fig. 2.5: The estimated applied N-fertilizers (annual average value 2001-2005) in the surface water bodies with the Model MONERIS (MUNLV, 2005b)

Fig. 2.6: Initial characteristics of Germany’s surface water bodies (EEA, 1999).

12 2.3 Geological Settings

The Lower Rhine Basin has a significant economical value as the Tertiary lignite is mined to be primarily used for electrical power generation. The Basin is a large northwest-southeast trending sedimentary basin. The Embayment separates the northern parts of the Variscian western and eastern Rhenish Massif. Regionally, the Lower Rhine Basin is extended to about 100 km long and 50 km width in Germany. This asymmetrical basin is subsided along oriented faults (see Figure 2.7) north west to south east forming several tectonic blocks: (1) the Block in the south with predominantly fluvial sedimentation in the Neogene, (2) Venlo Block in the northwest, (3) Erft Block, (4) Krefeld Block and finally in the southeast (5) Cologne blocks. These steep faults separate the areas of different tectonic subsidence and temperature histories (Klett et al., 2002). Figure 2.11 shows the location of the study area in the Venlo Block along the German borders with the Netherlands (Schäfer et al., 2005).

Fig. 2.7: The major structural elements and blocks in the Lower Rhine Basin (Schäfer et al., 1996).

13 The study area

Fig. 2.8: The geological layout of the study area in the Venlo Block within the Lower Rhine Basin (Schäfer and Siehl, 2002)

14 The basement is composed of folded Devonian and Carboniferous rocks, including coal- bearing Upper Carboniferous (Pennsylvanian) strata. Mesozoic rocks are only present in the south-western and northern part of the Lower Rhine Basin and uncomfortably overlie the older basement. The basin has been actively subsiding during the Tertiary and Quaternary and most of the Tertiary sediments of the basin remained sediment-starved during the early Tertiary. The basin is totally considered as a Tertiary rift incised about 80 km into the Palaeozoic basement of the Rhenish Massif. The significant sedimentation started in the Middle Oligocene were most of the marine transgressions were pronounced during the late Oligocene with more continental facies after the Middle Miocene. Fluvial sedimentation predominated during the Pliocene, when thick gravels and sands were deposited by palaeo- Rhine River. The entire rift sedimentary section consists of Tertiary and Quaternary unconsolidated fills with a maximum thickness of about 1500 m which rests non-conformably on a pre-existing Mesozoic to Palaeozoic basement (Schäfer et al., 1996; Wong et al., 2001). The Quaternary sediments are also composed of unconsolidated gravel, sand, clay, silt and peat. Some glacial drifts appear in the Quaternary formations in the area. A system of channels and valleys was incised into tertiary sediments by sub-glacial erosion. During the Quaternary times, these channels and valleys were refilled with sand and till. The German part of the basin which includes the study area contains about 1300 m of Oligocene to Pleistocene siliciclastic sediments with intercalated brown coal layers attaining a thickness of almost 100 m. The youngest Tertiary sequences are characterized by fluvial deposits from the Rhine and Maas and loess deposits (Wong et al., 2001). The Venlo Block (Venloer Scholle) is located in the western part of the lower Graben system and it is a part of the west- European plate. The Venlo Block is lower than the Krefeld one with about 150 to 200 m. The study area is located in the north-western part of the Venlo Block where the sediments of the older main terrace are light-grey fine gravely medium and coarse sand with varying-thickness layers of gravel. In the part of the Venlo Block the thickness varies between five and twenty meters. This terrace has pushed Mass River to the west until Krefeld. This terrace is separated from the younger main terrace through a clay layer. This layer has a variable thickness (from 4 to 25 meters) is composed of two clay or silt horizons divided by a layer of fine and middle sand (Gebka et al., 1999)

2.4 Hydrogeological Settings

The Lower Rhine Basin is one of the geological units from which the NRW is consisted. The study area, located in the Venlo Block, has almost the same hydrogeological features as the entire basin. As shown in Figure 2.9, Palaeozoic rocks of the Rhenish Massif (Sauerland and Bergisches Land) are entirely consolidated and therefore represent a very low groundwater

15 recharge potential because of the rocks nature which makes them impermeable for water recharge. Since the region of the Rhenish Massif generally exhibits low groundwater quantities because of the low infiltration rates of the consolidated rocks, the water supply is mainly from surface water reservoirs. The study area is characterized by unconsolidated rocks that were formed during the Cenozic era, these formations represents a highly yieldingly water-bearing unit and a valuable source of groundwater (Herrmann et al., 2009).

Fig. 2.9: The \major geological units found in the NRW (Climate & Environment Consulting Potsdam GmbH, 2006)

In the central part of the Westphalia Basin also Mesozoic consolidated rocks are present. In the unconsolidated rock regions water supply is based on groundwater withdrawals especially in the northern as well in the western parts of the Lower Rhine Basin. Tertiary sediments are mainly sea sands and brown coal deposits. Quaternary sandy and gravelly formations overlie Tertiary deposits and intervened with some clayey deposits. A well- developed flight of Tertiary and Quaternary terraces of Maas and Rhine reflects both changes in uplift rates and environmental conditions (Choi et al., 2007). The sediments of the older Lower Terrace and younger Lower Terrace represent braided river deposits formed during ties of much colder climate conditions than present (see Figure 2.10).

16

Fig. 2.10: Lower Rhine Basin with Pleistocene terraces (Boenigk, 2002)

Since the subsidence in the Lower Rhine Basin started, up to 1200 m Tertiary and up to 100 m Quaternary sediments have been deposited. The stratigraphic Tertiary sequence (see Figure 2.11 and Fig. 2.12) shows sequences of terrestrial sediments, transported from the south and marine sediments, transported from the north. These sediments are mostly sandy gravelly or clayey. The Quaternary sediments in the southern part of the Lower Rhine Basin are mostly fluvial deposits from the Rhine-Maas river system. Push moraines and aqueological deposits of the Saale inland ice have been added in the northern part of the basin. Aeolian sediments cover all the above mentioned sediments in almost all the basin.

17 Such sequences of permeable and impermeable layers represent a typical multi-layer aquifer with the Rhine River as the major receiving stream.

Fig. 2.11: Stratigraphy and terraces order in the Lower Rhine Basin (Boenigk, 2002)

2.5 Groundwater: Aquifer Systems

There are two major aquifer systems in the study area separated from each other with a clay layer (Flöz Morken, 6A) which acts as an aquiclude. However, this layer disappears in the north-western region of the study area and allows the two major aquifers to be hydraulically connected as the clayey layer is replaced with sandy deposits. The upper major aquifer is mainly composed of Pleistocene fluvial sandy and gravelly sediments and is classified into five sub aquifers due to the appearance of some interbedded impermeable clayey layers. The shallowest aquifer (layer 16) is mainly composed of the Main Terrace deposits (sandy to gravelly unconsolidated sedimentations) and has a thickness of about 10 to 17 m. The deposits of the Main Terrace are thicker in the Venlo Block than in the Krefeld block because of the fault system at the Viersen Heights. A layer of clay (Tagelenton Reuver-C-Ton, 13C) with about 10 m thickness separates the first aquifer (mentioned above) for a deeper sub- aquifer (the second sub-aquifer, layer 14, (13B)). The two sub-aquifers become hydraulically connected in northern part of the Venlo Block where the clayey layer disappears. The sand and the gravel sediments forming the first aquifer are highly conductive with a high recharge

18 rate of about 7 L/s. Groundwater flows from the south to the north and to the northern-west in the study area. As shown in Figure 2.13, a clayey layer (Rottserie 9A-C) separates the second sub-aquifer the third sub-aquifer which is composed mainly of gravel deposits (Hauptkiesserie 8). However, these two sub-aquifers are hydraulically connected in most of the Venlo Block as the clayey layer occurs only the north-eastern part of the block. There are also two sub-aquifers compose mainly of marine fine sandy deposits and separated from each other through a clayey layer (Flöz Morken, 6A).

Fig. 2.12: Stratigraphical view of the study area and the Venlo Block (modified after Klostermann, 2005).

19

Fig. 2.13: Stratigraphical view of the study area and the Venlo Block (modified after Klostermann, 2005).

20 2.6 Groundwater Abstraction and Nitrate Pollution

Groundwater is considered the major source for water supply and provides water for domestic use, agriculture, and industry in Germany; it represents approximately 75% of total Germany’s drinking water and is an integral component of the water cycle. The continuous abstractions together with the anthropogenic activities represent main sources for groundwater pollution in the study area. The current intensive agricultural applications lead to excess nitrate in soil and groundwater. Nitrate is now a main pollutant in NRW and the neighbour European countries. Over 50,000 instances of deleterious soil changes have been identified in North Rhine-Westphalia (MUNLV, 2005c). Germany has an adequate groundwater reservoir in term of quantity for future generation. However, approximately 52% of the Germany’s groundwater assessed is at risk (see Figure 2.14). Most pressure on groundwater bodies stems from different sources (urbanization, landfills, wastewater and solid wastes, and nonpoint agricultural agrochemicals). Recently, the Federal Environmental Agency conducted a groundwater monitoring network and compared it with the monitoring network done by the Joint Water Commission of the Federal States and concluded that more than 60% of the observed sites had a nitrate concentration higher than 50 mg/L (see Figure 2.15) and about 37% of the sites showed an upward trend where nitrate increases in groundwater (Wolter et al., 2000; LAWA, 2004).

Fig. 2.14: Characterization of groundwater in Germany in terms of quantity and quality according to the Water Framework Directive WFD (BMU, 2005).

Nitrate concentrations in groundwater in the North Rhine-Westphalia have dramatically increased in the period extending from 1993 and 1997. The upper allowable nitrate limit in - drinking water (50 mg/L as NO3 ) has already been exceeded in many parts of NRW as well as in Germany (Wendland et al., 1998). Use of fertilizers (organic and inorganic) is continuously increasing in NRW to ensure high crop productions. The amounts of the applied mineral fertilizers (mainly nitrate) are always higher than the organic fertilizers (MUNLV, 2005a). Recently, the Federal Ministry for the Environment, Natural Conservation and

21 Nuclear Safety published a report classifying groundwater quality in terms of nitrate pollution over the period extending from 1998 to 2005 and proved a decreasing trend in nitrate pollution in about 80% of the sampled areas and an increasing trend in the rest of the sampled area which requires an urgent action for groundwater quality management (BMU, 2006).

Fig. 2.15: Frequency distribution of nitrate concentrations measured at the stations of the Federal Nitrate Groundwater Monitoring Network (Wolter et al., 2000)

Figure 2.17 shows nitrate concentration in groundwater system in the study area. Moreover, groundwater in the study area is classified to have deteriorated quality not only in term of nitrate, but also with regard to other chemical pollutants; nitrate has also an increasing trend in the study areas which indicates a continuous deterioration of groundwater (BMU, 2006; MUNLV, 2008). The high degree of hydraulic connection between surface water groundwater aquifers increases the potential of aquifer contamination. The thickness of the unsaturated zone around the valleys ranges from less than 1 m to about 3 m and exceeds 25 m away from these valleys. The results of model calculations for the current situation of nitrate in groundwater show high potential for high nitrate pollution of the soil and groundwater (>50 - mg/L as NO 3). Moreover, groundwater pollution with nitrate is to be expected in all regions of North Rhine-Westphalia as they are subjected to intensive agricultural use (BMU, 2008). In order to achieve a sustainable use of water resources, effective strategies to reduce the nitrogen surpluses from agriculture must be developed and analyzed with respect to their spatial and temporal impact on the nitrate pollution of soil and groundwater. This should also consider various agricultural applications as well as the different hydrological, hydrogeological and agricultural condition (BMU, 2006).

22

(a)

(b)

Fig. 2.16: (a: above) Groundwater quality in terms of chemical pollution including nitrate (>50 mg/L); and (b: below) Groundwater quality in terms of nitrate where the study area also shows nitrate concentration in groundwater exceeding the allowable limit (MUNLV, 2008).

23 3. THEORY & LITERATURE REVIEW

3.1 Introduction

Groundwater pollution has overwhelmed the safe use of water resources around the world. There are different sources and types of pollutants that could occur on the land surface due to a wide range of human activities. These pollutants can work their way down into groundwater. Movement of water and dispersion within the aquifer spreads the pollutants over a wider area. However, recharge water causes pollutants dilution which could be partially or fully attenuated or eliminated through their residence in the subsurface environment. Additionally, there are different processes which control pollutants behaviour in subsurface and depend on porous media and pollutant properties. Therefore, the hydrogeological setting of the system plays an important role in the transport and the transformation of groundwater pollutants. There are several methods applied to assess groundwater vulnerability to pollution as a basic requirement for effective groundwater management. DRASTIC is one of the indices used to evaluate the intrinsic groundwater vulnerability. DRASTIC index will be discussed in this chapter with an overview of its previous different application. Modification of DRASTIC and some other indices will be also reviewed with more focus on the applications that are related to nitrate as a major groundwater pollutant. Therefore, nitrate retardation, sorption and reduction are also going to be discussed.

Groundwater pollution or contamination is defined as any adverse change in groundwater quality. These changes impair water quality to the extent it does not meet health and environmental standards. Such undesirable change in groundwater quality results from natural environmental agents or human (anthropogenic) activities. Groundwater in most of the European countries is of high concern as it represents a major source of water supply. However, groundwater in Europe is currently under several significant stresses which are generated from a wide range of human activities. Nitrate represents a significant problem for drinking water in some European regions where its concentration in groundwater exceeds the gridline level. Moreover, there is a wide range of pesticides used in different European countries where the maximum allowable residue has been exceeded throughout Europe. Groundwater is an essential natural resource in Germany as it represents the sole source of water supply for over two-third of the population as around 74% of drinking water comes from groundwater, making it Germany’s most important drinking water resource. Quantitatively, there are no problems in groundwater reservoir in Germany as a balance between groundwater abstraction and the formation of new groundwater exists. Assessment of groundwater status within the context of the 2004 analysis of the German Water Framework

24 Directive covering all river basins revealed that approximately 95% of all assessed bodies of groundwater achieve a good quantitative status. However, groundwater quality is endangered by the seepage of different pollutants into the groundwater bodies. A lot of efforts and many actions are put onto the ground in order to reduce the infiltration of pollutants derived from excessive fertilization of agricultural land, abandoned industrial sites, post-mining areas or military areas (EEA, 1999). About 52% of groundwater bodies in Germany are classified to be at pollution risk or to be already polluted (see Figure 3.1). A governmental working group in the field of groundwater has published a comprehensive report about nitrate in groundwater resources in Germany. This report indicated an - increasing trend in nitrate concentration in groundwater (0.5 to 1 mg NO 3/L per year over the last few decedes (LAWA, 1995).

Fig. 3.1: Characterization of the German surface water bodies and groundwater aquifers (BMU, 2006).

25 3.2 Groundwater Vulnerability

Vulnerability is a general term used to describe how a certain system is sense to any kind of stress. The term vulnerability was first used in hydrogeological applications in the late 1960’s, and since then it has been widely used in the 1980’s. Nowadays, this concept is commonly used and widely applied to assess protection potential of aquifer systems all over the world. Various definitions for groundwater vulnerability have been proposed, but still there is no common definition in use. The European Commission presents a summary of the various available definitions of groundwater vulnerability (COST, 1995). However, the most often- used definition is that from the United States National Research Centre (US-NRC, 1993) and which defines groundwater vulnerability as follows: “Groundwater vulnerability is the tendency of or likelihood for, contaminants to reach a specific position in the groundwater system after introduction at some location above the uppermost aquifer”.

3.2.1 Concept of Groundwater Vulnerability

The basic concept of aquifer vulnerability is the natural inherent ability of hydrogeological systems to provide a certain degree of protection through their characteristics. The overlying earth materials are a key factor in determining pollution likelihood of a certain aquifer; these layers are a media for a set of physical and biochemical processes which tend to retard or attenuate pollutants and therefore protect the aquifer. Protection degree provided by a certain hydrogeological system could be presented through groundwater vulnerability mapping which reflects the relative degree of exposure of a certain aquifer to pollution (Harter and Walker 2001). There are many examples where groundwater vulnerability has been mapped in many regions in the world (Babiker et al., 2005; Al-Hanbali and Kondoh, 2008; Gogu and Dassargues, 2000; El-Naqa et al., 2006; Lake et al., 2003; Ceplecha et al., 2004; Zekster et al., 2004; Napolitano and Fabbri, 1996). Groundwater vulnerability is not a parameter that can be directly measured in the field or in the laboratory. Nevertheless, vulnerability assessment depends mainly on the evaluation of different hydrogeological factors thought to contribute in aquifer protection (Focazio et al., 2002). Some of these factors are soil material, geological formations of unsaturated zone and aquifers, depth of groundwater table and recharge rate (Denny et al., 2007). This type of groundwater vulnerability, which considers only the inherent hydrogeological settings and does not refer to any specific pollutants, is known as intrinsic groundwater vulnerability. Intrinsic vulnerability differs from specific one in that the first doesn’t tend to differentiate between pollutants and describes only the inherent protection degree of the system without considering certain pollutants’ properties. Intrinsic groundwater vulnerability is known also as groundwater susceptibility or as aquifer sensitivity (Voigt et al., 2004). On the other hand, specific

26 groundwater vulnerability considers a specific pollutant or land use practice and integrates the corresponding properties in the assessment process (US-NRC, 1993). Groundwater vulnerability mapping is a tool which identifies highly susceptible area and delineates the parts of a groundwater aquifer which deserve groundwater protection action firstly. Afterward, the highly sensitive zones could be targeted with greater attention and urgent protection actions could be performed. Thus, instead of applying universal protection measures for the whole entire system, it is more cost-effective and time-saving to focus on the highly vulnerable zones (Foster et. al., 2003). Vulnerability mapping also provides a helpful tool for decision makers and act as a scientific base for a priority-based management program (Almasri, 2007b). Therefore, delineating a groundwater aquifer according to its susceptibility to become contaminated as a result of human activities is actually a basic requirement for efficient and effective groundwater management (US-NRC, 1993; Rupert, 2001; Herlinger and Viero 2007).

3.2.2 The Purpose of Groundwater Vulnerability Assessment

Vulnerability assessment is a general planning instrument and a decision-making tool. The objective of vulnerability assessment is to direct regulatory, monitoring, educational and policy development efforts to those areas where they are most needed for the protection of groundwater quality. The purpose of groundwater vulnerability assessment is often to differentiate between areas that need protection from potential contaminating activities, and areas where such activities that constitute a minor threat to the groundwater and are capable to sustain more time than more vulnerable zones. Therefore, vulnerability assessment is included within the traditional efforts for groundwater protection (Lindström and Scharp, 1995; Foster et al., 2002). Two important laws regard groundwater vulnerability were outlined by the US-NRC, (1993): all groundwater is to some degree vulnerable; uncertainty is inherent in all vulnerability assessments. These laws refer to the danger, especially when using complex vulnerability assessment tools that in light of the final vulnerability ranking one may lose sight of the data used for the analysis and of the assumptions underlying vulnerability assessment schemes. However, in spite of these reservations, vulnerability assessments are often recommended as an initial step in groundwater protection (e.g. US-NRC, 1993; US- NRC, 1995; Lindström and Scharp, 1995; Vrba and Zaporozec, 1994). Many groundwater professionals have argued about the usefulness of vulnerability assessments. It has been claimed that the hydrogeological conditions are too complex to be encapsulated by the simple vulnerability tools. It has also been questioned if it is possible to present a single, integrated vulnerability index or if it is necessary to work with specific vulnerability maps for individual contaminants. Scientifically, it is more consistent to evaluate vulnerability to

27 contamination by each contaminant or group of contaminants. However, the implication would be an atlas of maps for any given area, which would be difficult to use in most applications (Foster et al., 2002). Moreover, there will normally not be adequate data and/or sufficient human resources to achieve this idea. Finally, groundwater vulnerability assessment is still a basic requirement for groundwater management.

3.2.3 Vulnerability Assessment Methods

Although there are several different approaches to assess groundwater vulnerability, there is no universal methodology for groundwater vulnerability assessment. The methods are usually grouped into three major categories: (1) index and overlay indices, (2) process-based simulation models and (3) statistical approaches. Each category has advantages and limitations, and none are considered to be most appropriate for all situations. They serve different purposes and have different aims. Choosing with which methods the groundwater vulnerability is going to be assessed depends mainly on the hydrogeological settings of the natural system and on the availability of data.

(a) Index (Overlay) Methods

Index and overlay methods are based on the assumption that a few major parameters largely contribute in groundwater protection or affect groundwater vulnerability, and that these parameters are known and can be evaluated. These methods are generally based on limited basic regional data and usually cover extensive and regional areas. These methods assess groundwater vulnerability qualitatively and using relative scale. The collected hydrogeological information and parameters are classified according to a certain scoring or ranking system. An overlay vulnerability index will identifies areas where parameters indicating high vulnerability and some indices will assign numerical scores based on several parameters. In other words, these methods integrate factors contributing in groundwater protection and assign a weight for the hydrogeological parameters which depends on the protection degree provided by each parameter. Afterward, all parameters are superimposed in a numerical ranking system. Thus, an index method produces a dimensionless vulnerability map, a relative overview of vulnerability degree (GAO, 1992; US-NRC, 1993). The most commonly used of those methods is DRASTIC which uses a scoring system based on seven hydrogeological characteristics of a region (Aller et al., 1987). Several other overlay and index systems for groundwater vulnerability also exist and are being applied in different regions in the world. Examples of these methods are GLA (Hölting et al., 1995), GOD (Foster, 1987), SINTACS (Civita, 1994) and EPIK (Doerfliger et al. 1999). Typically, such systems include variables related to groundwater recharge rate, depth to the groundwater

28 table, and soil and aquifer properties. In general, index and overlay methods rely on simple mathematical representations of expert opinion and not on representation of physical processes. The advantage of these methods is that they provide relatively simple algorithms or decision trees to integrate a large amount of spatial information into maps of vulnerability classes or indices. Examples of vulnerability studies by index and overlay methods, conducted in many different regions in Europe (Rosen, 1994; Debernardi et al., 2007; Meinardi et al., 1995; Lake et al., 2003; Panagopoulos et al., 2006; Gogu and Dassargues, 2000; Stigter et al., 2006). The methods are particularly suitable for use with the Geographical Information System (GIS); they have been developed because of the lack of some monitoring data and due to the limitations to obtain more hydrogeological information. However, the lack of a physically-based and a precise representation has also some drawbacks. The results tend to be subjective and are based on personal professional judgment. If various methods are tested in one area, the resulting maps are often different and sometimes contradictory (Worrall et al., 2002; Ferreira and Oliveira, 2004; Gogu et al., 2003).

(b) Process-Based Models

Assessment of intrinsic vulnerability using overlay and index methods is independent of pollutants nature. The overlay and index methods are still disputed and were criticized in different previous studies. Process-based simulation models utilize the mathematical relations that govern the most important processes relevant to water and contaminants behaviour in the subsurface system. These simulation models have the capability to quantify, predict and validate processes. The vulnerability is represented in terms of travel times, leachate concentrations or critical loads which is a quantitative assessment of pollution risk. Process-based tools help resources managers to develop a shared conceptual understanding of complex natural subsurface systems; they allow testing of management scenarios, predict conclusions of high risk and high cost environmental manipulations, manage remediation measures and protection actions and set priorities (Caminiti, 2004). Nowadays, there is a large spectrum of process-based models to predict flow and contaminant transport in the subsurface. A mechanistic model usually uses current scientific understanding to incorporate the most fundamental (i.e. mathematical-physical) descriptions of the important and relevant processes using the governing equations for water flow and contaminant transport. A functional model (box-models) uses simplified, less mechanistic treatments of some or all the relevant processes. By using a simplified, less mechanistic approach, the number of input parameters for the model may be reduced. In some cases, a clear distinction between mechanistic models and functional models may be difficult to make.

29 Thus, in practice, the distinction is made between more or less mechanistic models. The simple functional models are attractive because they require relatively less data, while the predictive capability of these models is questionable due to the semi-empirical nature of the process description. On the contrary, the key problem in using the more complex models operationally lies in their generally large data requirements (Refsgaard et al., 1999). Hence, applying transport models for management purposes implies a trade off between the wish to describe the involved processes as good as possible, and the need to design a not too complicated management tool. Consequently, important concerns in the use of models are the choice of modelling approach and the choice of the degree of complexity. Limiting factors for the effective use of simulation models in assessing groundwater vulnerability include the difficulty to obtain and manage large amounts of soil and groundwater data and the spatial heterogeneities that pervade in natural environmental systems. The dependence on the existence of abundant input data implies that when data is scarce, the obtained results will suffer from substantial uncertainty (Anderson and Woessner, 1992).

Groundwater vulnerability was estimated in a part of the main aquifer in Cairo, Egypt using intensive isotope investigations, the study indicated that the areas with thicker clay layers and lower permeability will retard pollutants and then will provide high protection function to the underlying aquifer (Sadek and Aldelsamie, 2001). Isotopes are also useful for different predictions involving contamination dynamics at specific sites. However, their need for extensive data input and the expertise required to implement them may, in some cases, limit their use over large areas (Lindström and Scharp, 1995; Refsgaard et al., 1999).

Root zone, organic matter content, microbial populations and oxygen concentrations were considered in solving the advection-dispersion equation which was simulated to provide a basis for groundwater vulnerability assessment (Connell and van den Daele, 2003). A three- dimensional model was developed to assess groundwater vulnerability in the southeast of Frankfurt am Main, Germany. The model considered different approaches which including the water retention period, behaviour of organic components and the amount of infiltration water available. The results were used to compute and map the local groundwater vulnerability and showed a good correlation with interpolated maps from drill data (Lerch and Hoppe et al., 2007). As a support for the European policy, a Meta-Model (EuroPEARL) of the spatially distributed pesticides leaching in Europe was developed. EuroPEARL considers transient flow and solute transport. The Meta-Model were applied to generate maps of the predicted leaching concentration in the European Union. Maps generated with the Meta- Model showed a good similarity with the maps obtained with EuroPEARL, which was confirmed by means of quantitative performance indicators (Tiktak et al., 2006).

30 Among the currently available simulation models, the PRZM model was specifically developed to simulate the fate of pesticides; its results were compared with the pesticide DRASTIC index. No significant correlation was found between their results. This weak correlation was attributed to that DRASTIC does not consider the chemical characteristics of the potential contaminants. The only common factors between the PRZM model and the DRASTIC index are the depth to groundwater and the vadose zone (Banton and Villeneuve, 1989). Groundwater vulnerability to pollution was assessed using the SEEPAGE Model approach. The model results showed a very good correlation (Muhammetoglu et al., 2002). Application of numerical simulations were applied in different areas to assess groundwater vulnerability (Stewart and Loague, 2004; Butscher and Huggenberger, 2008; Schwartz, 2006; Sacco et al., 2007; Stenemo, 2007). A modelling system for intrinsic groundwater vulnerability assessment was developed. The system consisted of flow and transport models for the unsaturated zone and the groundwater zone and was coupled to a Geographical Information System. The system was successfully used to demonstrate the vulnerability of different environmental conditions (Lindström, 2005). On a regional scale, the GeoPEARL model was used to assess the vulnerability of the Brusselian aquifer considering both land use and pesticide properties (Leterme, 2006).

(c) Statistically-Based Methods

Statistical methods are probably the least common category of vulnerability assessment methods found in the literature. Although statistical studies are used as tests for other methods and geostatistical methods and are frequently used to describe the distribution of water quality parameters, very few vulnerability assessment methods are directly based on statistical methods (Nolan et al., 2002; LaMotte and Greene, 2007; Assaf and Saadeh, 2008; Ta’any et al., 2009; Al-Kuisi et al., 2009). Statistical methods are used to quantify the vulnerability of groundwater contamination by determining the statistical dependence or relationship between observed contamination and environmental conditions that characterize vulnerability (e.g. unsaturated zone properties or recharge) and observed land uses that are potential sources of contamination (e.g. fertiliser application and septic tank occurrence). Once a model of this dependence or the relationship has been developed with statistical analysis, the probability of contamination can be evaluated. Knowledge of significant environmental conditions is required for the area in question. In statistical methods the vulnerability is expressed as contamination probability. The higher the contamination probability, the higher the vulnerability is (Burkart et al., 1999; Focazio et al., 2002; Masetti et al., 2007). Choosing an approach is directly affected by data availability and purpose of the evaluation (US-NRC, 1993). Furthermore, the statistical significance of the results can be

31 explicitly calculated. This provides a measure of uncertainty in the model. The disadvantage is that statistical methods are difficult to develop and once established, can only be applied to regions that have similar environmental conditions to the region for which the statistical model was developed. Teso et al. (1996) used a logistic regression model and GIS to predict groundwater vulnerability to pesticides. A statistical method, CALVUL, is used to determine the groundwater vulnerability due to pesticide leaching in California (Troiano et al., 1997). In Texas, Evans and Maidment (1995) developed a similar statistical analysis using nitrate in groundwater as the basis for delineating vulnerable groundwater areas. Linear regression was used to construct a relationship between the probability that nitrate concentrations in groundwater in Texas exceed a certain limit and the hydrogeological indicators. Groundwater vulnerability to nitrate in an area near Frankfurt-Main in Germany was assessed using statistical-based approach. Nitrate concentration in groundwater was evaluated as a function of land use, geology, soil types and depth to groundwater table (Schleyer, 1994).

3.3 Groundwater Vulnerability Using DRASTIC

3.3.1 Direct Use of DRASTIC

The DRASTIC index is probably one of the simplest overlay methods used to assess groundwater vulnerability. This index assesses the intrinsic groundwater vulnerability based on the inherent hydrogeological characteristics of a certain subsurface system (Aller et al., 1987). DRASTIC, in its standard form, has no ability to assess groundwater vulnerability for a specific pollutant or a particular hydrogeological feature. The DRASTIC index is now the most popular index method used to assess groundwater vulnerability. However, it is also the method which is subjected to different forms of adjustment, and therefore the strongest critique among hydrogeologists. A detailed description of the DRASTIC index methodology is found in chapter four. DRASTIC index is now a useful tool for identifying vulnerable groundwater systems. Therefore it was used to map vulnerability in different groundwater systems in different regions in the world. Groundwater vulnerability in the coastal aquifer in Gaza Strip; there was a poor correlation between vulnerability values and the actual nitrate concentration in the Gaza coastal aquifer. However, after considering the land use, was a better correlation between nitrate concentration and the DRASTIC index was achieved (Baalousha, 2006). Similar applications of DRASTIC have been performed in groundwater aquifers in United Arab of Emirates, Morocco, Tunisia, Algeria, Portugal and South Korea (Al-Zabet 2002; Kim and Hamm 1999; Kachi et al., 2007; Ettazarini, 2006; Stigter et al., 2006; Hamza, et al., 2007). Some groundwater basins in China, Japan and Iran were classified according their vulnerability using the DRASTIC index (Wen et al., 2008; Babiker et al., 2005; Chitsazam and Akhtari, 2008). Groundwater quality in the Upper Litany Basin in

32 Lebanon was also assessed based on geostatistical analysis of nitrate; the results showed a non-strong correlation to the DRASTIC vulnerability map for the same groundwater basin (Assaf and Saadeh, 2008). DRASTIC was also applied to assess groundwater vulnerability in different parts in Jordan as well as in South Korea. The parameter of the hydraulic conductivity was excluded from the DRASTIC index because of the lack of data and the DRASTI was only applied in the Azraq Basin in Jordan (Al-Adamat et al., 2003). The DRASTIC index was also used in a part of the South Korea to assess groundwater vulnerability to different landfills (Lee, 2003).

The effectiveness of DRASTIC vulnerability map was improved by calibrating the rating system on the basis of a statistical correlation between the standard DRASTIC vulnerability map and an actual data set of nitrate or other pollutants concentration in groundwater (USGS 1999; Rupert, 2001). Correlation of DRASTIC parameters with the actual nitrate concentration in Kherran Plain in Iran showed that the impact of the vadose zone is the most significant hydrogeological parameters in controlling nitrate concentrations in groundwater (Chitsazam and Akhtari, 2008). On the other hand, a very strong positive correlation between nitrate concentrations in the groundwater system in Kalamazoo County in the USA and the AQUIPRO vulnerability index was observed (Chowdhury et al., 2003). The standard weights of the DRASTIC index were modified in many areas after carrying out sensitivity analysis for the DRASTIC parameters (Pathak et al., 2008; Almasir, 2007a; Al-Kuisi et al., 2009). In a comprehensive vulnerability assessment of groundwater in the Northern Italy with intensive correlation approaches, it was concluded that the GOD vulnerability index is not able to analyze physical and biochemical processes controlling nitrate in the subsurface system (Debernardi et al., 2007). As discussed above, in spite of DRASTIC simplicity, there were several studies that attempted to modify and adjust the standard DRASTIC index to be used in groundwater vulnerability assessment for a specific pollutant or in a special hydrogeological setting. The standard DRASTIC index was incorporated with the land use index for a part of the coast aquifer to the north of Gaza Strip. The study integrated the impact of extensive land use to the DRASTIC index to assess the potential of groundwater pollution. The final assessment proved that the composite DRASTIC index exhibited indicated a close relationship to the actual groundwater pollution existing in the area. The vulnerability was specifically highly correlated to nitrate concentration in the upper aquifer (Secunda et al., 1998).

3.3.2 Specific Applications of DRASTIC and Other Indices

Guo et al., (2007) used DRASTIC with some modifications to map groundwater vulnerability to arsenic pollution in the Taiyuan basin in northern China. Three DRASTIC parameters were

33 replaced with other parameters. The new added parameters were ration of cumulative clay thickness, aquifer thickness, and sorption capacity. These modifications were based on that the new three parameters were more significant than the eliminated ones (Guo et al., 2007). The impact of discrete fracture and faults on the quality of groundwater resources were integrated in DRASTIC through generating the new DRASTIC-Fm index (Denny et al., 2007). Based on intensive correlation between nitrate concentrations in groundwater in a part of Trifilia province in Greece, it was found that adding the land use as a new factor and removing both hydraulic conductivity and soil media made the new DRASTIC index more suitable for the nitrate status in groundwater (Panagopoulos et al., 2006). There are other studies which modified DRASTIC to suit specific hydrogeological settings by adding new parameters, eliminating others, or both. The parameters of recharge rate and hydraulic conductivity were eliminated from the standard DRASTIC index, and the new DASTI index was used to assess groundwater vulnerability in the Mamora basin in Morocco (Kabbour et al., 2006). A significant difference was found between DRASTIC and GOD vulnerability maps in the Rio Artiguas basin in Sweden. DRASTIC was afterward modified by adding a new parameter to account for the geological lineament influence (Mendoza and Barmen, 2006).

Soil sorption capacity was integrated in the DRASTIC index to evaluate groundwater vulnerability to copper, lead, sulphate and phosphate in a certain groundwater system in Brazil. The soil sorption capacity for these ions was estimated using the traditional batch experiments (Herlinger and Viero, 2007). A nitrogen pollution index was generated by combining the DRASTIC index with the fertilizers application rates in Texas in the United States (Halliday and Wolfe, 1991). Aquifer thickness and impact of contaminant were added as new parameter to DRASTIC, at the same time both aquifer media and hydraulic conductivity were eliminated to assess groundwater vulnerability to organic pollutants in the Wuhan city in China, the new developed index was named DRAMIC as it involves the effect of organic matter content (Wang, et al., 2007). These results suggest that neither single parameters nor vulnerability methods (GOD and TOT) are able to describe individually the complex phenomena affecting nitrate concentrations in soil, subsoil and groundwater. In particular, the traditional methods for vulnerability analysis do not analyze physical processes in aquifers, such as denitrification and nitrate dilution. According to a recent study in the shallow unconfined aquifer of the Piemonte plain, dilution can be considered as the main cause for nitrate attenuation in groundwater (Debernardi et al., 2007). Groundwater vulnerability was estimated for the Manawatu groundwater aquifer in New Zealand using the sorption capacity an indicator of attenuation capacity. Sorption capacity data was correlated with cation exchange capacity and organic matter content (Bekesi and McConchie, 2000). A robust model was developed to delineate vulnerable zones to nitrate pollution. The model

34 involved spatial data on surface leaching, soil characteristics, drift cover and aquifer type (Lake et al., 2003). The vulnerability map toll (VM) was used to assess groundwater vulnerability to nitrate in Colorado groundwater aquifers. The VM uses five different parameters: aquifer location, depth to water, soil drainage class, land use, and recharge availability. There was a good correlation between the actual nitrate concentrations in groundwater (Ceplecha et al., 2004). The vulnerability of both soil and groundwater was assessed by establishing a ranking system of the combined risks of a diffuse contamination for the top soil and for groundwater. The factors involved were land cover, top soil features, net precipitation, aquifer type, groundwater recharge and age (Meinardi et al., 1995). Groundwater vulnerability and factors affecting it were assessed in a study of the Snake River Plain aquifer basin. The main investigated factors included in the rank system were depth to water, properties of the unsaturated zone, and groundwater recharge (Zekster et al., 2004).

3.3.3 Comparison of DRASTIC and Other Indices

As stated above, there are many other indices used to assess groundwater vulnerability to pollution. Some of these indices have also been compared with DRASTIC for the same study area. Examples are: GLA (Hölting et al., 1995), GOD (Foster, 1987), SINTACS (Civita, 1994), EPIK (Doerfliger et al. 1999), COP (Vias et al., 2005) and PI (Goldscheider et al., 2000).

The GLA index or Hölting-method has been established by the Geological Surveys of the individual states of the Federal Republic of Germany to assess the capacity of the covering layers including soil and the unsaturated zone to protect the underlying aquifer. The considered parameters are the field capacity of the top soil, groundwater recharge, and rock- related parameters (Hölting et al., 1995). The method has been applied and tested in several countries in the world and has proven its effectiveness and usefulness (Margane et al., 1999). The basic concept of the GLA index is that the overlaying layers have a certain capacity to reduce contaminant concentrations leaching to the groundwater table. This reduction capacity is a function of the travel time. Consequently, the protection capacity is a function of all parameters that control travel time of pollutants from the land surface to the groundwater table.

The PI index was developed for the European karst aquifers and has been modified to suit the semi-arid regions. It considers two major factors; the P-factor considers the groundwater table overlaying layers, and the I-factor which considers the infiltration conditions (Goldscheider et al., 2000). The PI has been used in different regions in the world including

35 some semi-arid zones (Goldscheider, 2005). The COP index was developed to assess intrinsic groundwater vulnerability in different karst areas (Vias et al., 2005). Afterward, the index has been modified by adding a new factor (K) which considers the saturated karst groundwater through gathering information on water flow paths, travel times and recovery rates (Andreo et al., 2009). The SINTACS is another point ranking system for the groundwater vulnerability assessment (Civita et al., 1990; Civita, 1993; Civita et. al., 2004). SINTACS represents the same parameters of DRASTIC and was also applied in different regions in the world (Napolitano, 1995; Al-Kuisi et al., 2006; Cusimano et al., 2004; Cucchi et al., 2004; Corneillo et al., 2004; Uhan et al., 2008; Mahlknecht et al., 2006).

The GOD index related to the vertical pathways of pollutants to the saturated layer. It considers three parameters; groundwater occurrence, overall aquifer class, and depth to groundwater table (Foster, 1987). The method was also used in different regions to assess the intrinsic groundwater vulnerability (Ferreira et al., 2004; Ertekin, 2005; Mendoza and Barmen, 2006). The EPIK index has the same conceptual ranking and rating system of DRASTIC. However, EPIK is a multi-attribute method which addresses the specific hydrogeological behaviour of karst aquifers. EPIK considers four parameter; epikarst, protective cover, infiltration conditions, and karst network conditions (Doerfliger et al. 1999).

VURAAS is a relatively new index developed to assess groundwater vulnerability in karst areas in the Alp regions through considering three parameters; the input (P), the infiltration (I) and the exfiltration (E) (Cichocki, 2003). The method was also applied in different regions like Austria (Laimar, 2005). Many studies compared two or more indices in assessing groundwater vulnerability in the same basin. Six different vulnerability indices (AVI, GOD, DRASTIC, SI, EPPNA, and SINTACS) were applied in an aquifer system near Evora (Alentejo, Portugal). The results showed significant variation in the vulnerability maps of the applied indices, which emphasize the high subjectivity involved in applying the ranking system (Ferreira and Oliveira, 2004). The results obtained from another comparison between DRASTIC, GOD and AVI proved more reliability of DRASTIC index as it based on more hydrogeological parameters (Jimenez et al., 2005). There are many other studies which compared different indices in different types of groundwater aquifers. The studies agree that there is a significant difference between the final maps; this was attributed to that these indices are relatively not accurate and have a high degree of subjectivity. A more accurate and representative application of such methods requires considering more parameters that specifically reflect the behaviour, fate, and transformation of different pollutants (Margane, 1999; Gogu and Dassargues, 2000; Magiera, 2000; Magiera, 2002; Gogu et al., 2003; Vias et al., 2005; Ibe et al., 2001; Mendoza and Barmen, 2006).

36 3.3.4 Sensitivity Analysis of Vulnerability Indices

DRASTIC and the other indices used to assess groundwater vulnerability rely on grouping several hydrogeological parameters and probably use large numbers of data sets. The use of such high number of data layers reduces the uncertainty of the final vulnerability indices maps. Sensitivity analysis helps in investigating the relative importance of these parameters and the effectiveness of removing or adding new parameters. There are tow main approaches to assess two types of sensitivity for the vulnerability indices. Two sensitivity approaches are available; map removal (Lodwick et al., 1990) and single parameters sensitivity (Napolitano and Fabbri, 1996). Map removal is a type of sensitivity analysis which evaluates the sensitivity of the standard vulnerability map toward removing one or more of the DRASTIC parameters. Single parameter is the second type of sensitivity analysis that evaluates the significance of each of the DRASTIC parameters on the vulnerability map. This is achieved through comparing the theoretical weight with the effective one for each parameter. The EPIK index was used to assess groundwater vulnerability in a karst aquifer. A sensitivity analysis for the four parameters was also conducted to evaluate the influence of each single parameter on aquifer vulnerability assessment and to achieve reliable interpretation of vulnerability indices for groundwater resources in karst regions (Gogu and Dassargues, 2000). The DRASTIC index was used to assess groundwater vulnerability to nitrate pollution in the coastal aquifer in Gaza Strip. It was found that more than 75% of the aquifer can be designated as a moderately vulnerable area. Recharge rate and the impact of the vadose zone were found to be the most significant parameters. However, there was no correspondence between the vulnerability index and the actual nitrate pollution in the aquifer which was attributed to that DRASTIC does not consider any possible fate or transport of nitrate (Almasri, 2007a). The sensitivity analysis was also performed for the DRASTIC index after applying it in a small groundwater basin in Jordan to assess vulnerability to a certain landfill. The results showed that the area surrounding the landfill is highly vulnerable and that both thickness and impact of the vadose zone were the most dominating parameters (El- Naqa et al., 2006). The sensitivity analysis was also conducted for DRASTIC as well as for other indices in different regions in the world. According to these studies, different DRASTIC parameters were removed or adjusted for their weights and rates (Babiker et al., 2005; Bazimenyera and Zhonghua, 2008; Al-Hanbali and Kondoh, 2008; Pathak et al., 2008; Kabera and Zhaohui, 2008).

3.3.5 GIS in Vulnerability Assessments

Hydrogeological investigations and interpretation entered a new era with the advent of Geographical Information System (GIS). GIS is a computer-based system that is designed to

37 capture, store, retrieve, manipulate and integrate spatial data. GIS includes five essential components: data acquisition, pre-processing, storage and retrieval, manipulation and analysis, and product presentation (Sokol et al., 1993; El Kadi et al., 1994; Morse et al., 1994; Zhang et al,. 1996, Loague and Corwin, 1998; Corwin et al., 1999; Lasserre et al., 1999; Lord and Anthony, 2000; Tiktak et al., 2002; Wendland et al., 2005; Manguerra et al., 1997). Goodchild (1993) defines a GIS system as a tool for handling geographic and tabular data in digital form with the following capabilities: (i) data pre-processing including reformatting, change of projection, re-sampling, and generalization, (ii) direct support for analysis and modelling, and (iii) post-processing of results.

Natural resources and environmental concerns, including hydrogeological investigations and groundwater management, have benefited greatly from the use of GIS. Typical examples of GIS applications in groundwater studies are site suitability analyses, managing site inventory data, estimating vulnerability of groundwater to pollution potential from nonpoint sources of pollution, modelling groundwater movement, modelling solute transport and leaching, and integrating groundwater quality assessment models with spatial data to create spatial decision support systems (Engel et al., 1996).

Vinten and Dunn (2001) studied the effects of land use on temporal changes in well water quality. Levallois et al., (1998) studied groundwater contamination through nitrates associated with intense potato culturing in Quebec, Canada. The data analysis was carried out by combining GIS and statistical methods. Ahn and Chon (1999) investigated groundwater contamination and spatial relationships among groundwater quality, topography, geology, land use, and pollution sources using GIS in Seoul, Korea. Ducci (1999) produced groundwater contamination risk and quality maps by using GIS in Italy. Fritch et al. (2000) developed an approach to evaluate the susceptibility of groundwater in north-central Texas to contamination. Simulation models of water and pollutants in the subsurface system were coupled with the GIS in a groundwater basin in Sweden to develop a management tool (Gumbricht and Thunvik, 1997; Eliasson, 2001; Sivertun and Prange, 2003; Thunqvist, 2003; Lindström, 2005). GIS provides the assessment of groundwater vulnerability with an efficient tool in data handling, analytical capability and display flexibility. Examples of areas where GIS has been used in groundwater vulnerability assessment are: (i) integration of various data layers that are involved in the vulnerability assessment, (ii) support of analysis and modelling of spatial and physical relationships of critical environmental variables and parameters and (iii) display of results in the form of maps.

38 3.4 Groundwater Nitrate Pollution

Nitrate is a naturally-occurring ion and is a major part of the nitrogen cycle. Because it is very soluble, nitrate is the most usable form of nitrogen for plants. Nitrate is a common surface water and groundwater contaminant that can cause health problems in infants and animals, as well as eutrophication in surface waters (Fennesy and Cronk, 1997). Nitrate is an effective fertilizer and is used in agricultural activities due to the use of fertilizers and manure application. However, there are other nitrate sources related to urban development that can increase nitrate concentrations in groundwater. Some studies in the last few years have found that nitrate concentrations in some urban aquifers are similar or even higher to those in their surrounding agricultural areas (Ford and Tellam, 1994; Lerner et al., 1999). The wide range of nitrate sources and the complexity of their distribution make the estimation nitrate loads as well as assessing input to groundwater a difficult task. There are different nitrogenous forms which go through one cycle and in a rate depending on local characteristics of the soil-water system. Agriculture is the major source of nitrate, but it is not the only one. There are several sources including sewage and mains leakage, septic tanks, industrial spillages, contaminated land, landfills, river or channel infiltration, fertilizers used in gardens, house building, storm water and direct recharge. An overview of these sources is presented in details in the section 3.4.2. Within the last decades, nitrate use has gone beyond the plants needs and the capacity of the biosphere to assimilate or eliminate it. Nitrate is carried with the flowing groundwater water and might undergo different biochemical processes. Sorption and denitrification are the only two processes in which nitrate is retarded and reduced in subsurface systems. Understanding nitrate dynamic and assessing the related parameter in the subsurface system is of great importance for groundwater management and protection and remediation. The following sections focus on nitrate sources, human and environmental impacts and processes occurring in soil-water systems particularly sorption and denitrification.

3.4.1 Nitrate: Health and Environmental Impacts

Nitrate, itself, has a low toxicity except at massive doses and is generally of no concern with respect to human health. However, under certain circumstances nitrate can be reduced to nitrite and acts as a main cause of blue-baby disease and might have some contributions to stomach and colon cancer (WHO, 1998; Yang et al., 2007). The immediate health concern of nitrate is therefore through its reduction to nitrite in the digestive tract by the active nitrate- reducing bacteria. Thus, nitrite and N-nitrosamines compounds coexist often with nitrate and are biologically active in human body (Sprent, 1987; McLay et al., 2001). Nitrite is readily absorbed into the blood where it combines with the haemoglobin and converts it into

39 metahemoglobine which is not able to carry oxygen. This phenomenon is a well-known disease especially among infants and is known as blue-baby syndrome or methemoglobinemia. Nitrate is therefore directly linked to the blue-baby syndrome, the case in which human blood is not able to carry oxygen (Curry, 1982; White and Weiss, 1991). The blue-baby syndrome can result in a blue-grey colour in the infants’ skin and is associated to nitrates even in small intake doses ranging about 25 mg/L as nitrate-nitrogen. These syndromes are rarely observed on adults where the low gastric acidity and the high-active enzymes inhibit nitrate-reducing bacteria. Nitrate-reducing bacteria are abundant in the infants’ stomachs of humans as well as animals (cattle, horses, sheep, baby pigs, and baby chickens). Therefore, they are more susceptible to nitrate poisoning. With time, the digestive system will start to produce acids which hinder the function of these bacteria to thrive and the health risks are reduced for children older than six months of age and adults (WHO, 2006; US-EPA, 2003; MOH-NZ, 2006).

The Britain Royal Commission on Environmental Pollution has documented the last ten cases of blue-baby syndrome occurred in the (UK) in 1979, one of them was fatal. No cases occurred after that where nitrate concentration in drinking water were less than 100 mg/L (Croll and Hayes, 1988). Two case of methemoglobinemia in newborn infants were recorded in and appeared to be caused by high nitrate level in the well water used for the dilution of powdered milk (Ferrant, 1947). It was also found that more than 15% of infants in rural areas of New York State and other regions in the USA who drink well water are at risk for methemoglobinemia from being exposed to high level of nitrates in the drinking water (Fan and Steinverg, 1996; Gelberg et al., 1999). Because nitrate in groundwater is a serious problem in Gaza Strip, a field study was performed to investigate blue-baby cases caused by high nitrate concentrations in drinking water. The experimental results indicated that the prevalence of methemoglobinemia is very high and more than 70% of the tested infants were suffering from a blue-baby syndrome (Al-Absi, 2008).

The relationship between nitrate levels in drinking water and cancer has been inconclusive. Nitrogen-nitrosamine compounds are some of the strongest known carcinogens. They have been found to induce cancer in variety of organs in various animal species including higher primates (Jalali, 2005). Consequently, nitrates may also have a possible role as pro- carcinogenics (Almasri and Kaluarachchi, 2004). It was documented that there was no significant difference in stomach cancer rates between a high nitrate area and a similar low nitrate area in the UK. A study on the incidence of cancer in Britain farmers working in a fertilizer plant showed that no significantly higher cancer rates were observed in a control group of similar workers (Croll and Hayes, 1988). Moreover, in a study which was designed

40 to look at the relevance of nitrates to gastric cancer in the UK, no clear positive trend was recorded between nitrate and nitrate-related nitrosamines and the carcinogenesis of gastric tumours (Forman et al., 1985). Another study showed that there was no significant association between nitrate concentration in drinking water and the risk of death from colon cancer (Yang et al., 2007). Despite many uncertainties regarding the association of nitrate intake and cancer (Zeegers et al., 2006), nitrate in drinking water is still considered as a risk factor. Intensive experimental data suggests a role for nitrate in the formation of carcinogenic N-nitrosamines and stated that high concentration of nitrate in drinking water can not be excluded as a factor in analysing gastric cancer (Clough 1983; Weyer 2001). Moreover, a positive correlation between nitrate concentration in drinking water and colorectal cancer has been found Trnava District in Slovakia (Gulis et al., 2002). Finally, the World Health Organisation (WHO) has stated that there is still no convincing evidence of an association between gastric cancer and high doses of nitrate, but this association could not generally be excluded (WHO, 1998). Nitrate in the surface water bodies is also undesirable as it causes eutrophication and poses a real problem for the aquatic life (MacQuarrie et al., 2001; Edwards et al., 2006). Nitrous oxide that may result from nitrate denitrification will contribute in the destruction of the stratospheric ozone (Tindall et al., 1995). The WHO has established - a drinking water standard of 50 mg NO 3/L as nitrate which is corresponding to 11 mg/L as - NO 3-N (WHO, 2006). The U.S. Environmental Protection Agency (US-EPA) has - promulgated the allowable nitrate concentration in drinking water at 45 mg NO 3/L which is - corresponding to 10 mg/L as NO 3-N (US-EPA, 2003).

3.4.2 Origins of Nitrate in Groundwater

Nitrogen is a major constituent of the earth’s atmosphere and is one of the most important vital elements without which organisms can not survive (Delgado, 2002). Worldwide, nitrogen is the plant nutrient most critical to the production of food and fibre (Lake et al., 2003; Schröder et al., 2004). The major nitrogen source is the atmosphere, as it represents more than two thirds of the extant atmosphere. Nitrogen deposits in soils during the process of soil formation, through deposits from rainfall and through plants and microbial fixation of nitrogen (Boyer et al., 2004). Nitrogen accumulates also in soil organic matter as a decay by-product of plants and animals. Organic nitrogen compounds are also dominant in soils where it represents about 98% of the total nitrogen in soil body. Soil, water and microorganisms contain nitrogen in its gaseous, reduced or oxidized forms. However, plants and some - microorganisms are only able to mainly consume nitrate (NO 3) which enters the soil body either naturally or through different anthropogenic processes (Sprent, 1987). Table 3.1 summarize the different forms of nitrogen and there occurrence.

41 Potential sources of nitrate in soil and groundwater include naturally-occurring nitrate, nitrate derived from fertilizers and manure, septic tanks and wastewater facilities, soil wastes and landfills. Nitrate reserves of 2000 kg-N/ha to more than 25000 kg-N/ha are stored in soil of the temperate zones depending on the thickness of soil, climate, and clay and organic matter content. About 98% of these reserves are found in the form of organic nitrogen and the rest is in the form of nitrate and ammonium. Transformation processes take place between these different forms of nitrogen in the so called nitrogen cycle, nitrate will eventually be formed in soil under certain condition and the excess nitrate will then leach to the aquifer system (Stahr et al., 1994).

Table 3.1: Nitrogen forms and their occurrence

Form Notes

Gases (N2) Dinitrogen (nitrogen gas) is the most common form. It makes up 79% of the

atmosphere but cannot be used by plants. It is taken into the soil by bacteria,

some algae, lightning, and other means.

(NO2, NO) Exist in the atmosphere either free or attached to water or soil particles.

Reduced Ammonia is the main reduced form of nitrogen. All the above nitrogen gases

(NH3) may also present in water regardless degree of saturation

Nitrate Is the main oxidized form of nitrogen; it has high water-solubility and low

- (NO 3) reactivity. Nitrate is the form of nitrogen most used by plants for growth and

development. Nitrate is the form that can most easily be lost to groundwater.

Ammonium Ammonium is taken in by plants to be used directly. This form is not lost as

+ (NH 4) easily from soil as it is always attached to negatively-charged soil particles.

Organic Organic nitrogen forms exist in many different forms (proteins, urea, amino

Nitrogen compounds, and polymers. It is changed into ammonium, then into nitrates,

by microorganisms. Both of these inorganic forms can be used by the plant.

Fertilizers and manure are intensively used to increase crop yield as a result of increasing public demands and that tend to increase in the future (Almasri, 2007a). Overuse of fertilizers and manures leads to nitrate leaching from the upper protective zone and therefore to groundwater contamination with nitrate (Todd, 1980). Hence, agriculture is the main non- point source of elevated nitrate levels in groundwater (Wakida and Lerner, 2005; Almasri and Kaluarachchi, 2004).

42 Animals are used in many parts of the world. In some parts, cats and dogs are used as domestic animals. On the other side and in developing countries, donkeys, horses and cows are used in rural areas and as transport means, some animals are used for food productions. Excreta, dung, and urine produced by these animals constitute a variety of pollutants, of which nitrate which will be stored in soil body and leach under suitable conditions. Nitrogen is converted to nitrate by different bacteria living in soil. The growing plants as well as bacteria consume a part of these nitrates. When sufficient decomposable organic matter is present, soil bacteria can remove a significant amount of nitrate-nitrogen through a process called immobilization in which nitrate becomes a part of soil organic matter. Another group of bacteria use nitrates as a substitute for oxygen when the free molecular oxygen is limited. These bacteria convert nitrate to nitrite and nitrogen gases such as nitrogen, nitrous oxide, and nitrogen dioxide.

This conversion of nitrate-nitrogen to gaseous form is known as denitrification. Nitrate which is not taken up by crops, immobilized by bacteria into soil organic matter or converted to atmospheric gases by denitrification will leach out of the root zone and possibly end up in groundwater (Chowdary et al., 2005). Nitrate is formed wastes and sewage rich of nitrogen are biologically transformed under aerobic conditions. Contaminated lands, especially landfills, have also a significant contribution to nitrate concentrations in groundwater. A typical ammonium concentration in a landfill leachate reaches 1250 mg/L. Wastewater accumulation in the treatment ponds and leakage of wastewater in sewer systems will eventually increase groundwater nitrate concentration (Todd, 1980; Onsoy et al., 2005; Porporato et al., 2003; Rodriguez et al., 2005; Widory et al., 2005). There are several studies which showed a strong association between nitrate concentrations in groundwater and the above soil layers and land use categories including different agricultural activities, urban areas, and landfills.

Table 3.2 is a summary of potential sources of nitrate in groundwater from some different reference and studies available in the literature (Baker, 1992; Van der Schans et al., 2009; Postma et al., 1991; Harter et al., 2002; Ling and Al-Kadi, 1998; Joosten et al., 1998; Jordan and Smith, 2005; Liu et al., 2005; Khayat et al., 2006; Wakida and Lerner, 2005; Jalali, 2005; McLay et al., 2001; Sugimoto and Hirata, 2006; Chen, et al., 2003; Andersen and Kristiansen, 1984; Benson et al., 2007; Hiscock et al., 2007; Singh et al., 1995; Thorburn et al., 2003; Hund-Göschel et al., 2007; Jungkunst et al., 2006).

43 Table 3.2: Source and origins of nitrate in soil and groundwater

Source Diffuse (Nonpoint sources) Non-Diffuse ( Point sources)

Agriculture  Use of industrial fertilizers  Accidental spills of nitrogen-rich

 Use of organic fertilizers compounds

(manure and slurries)  Absence or leakage of slurry and

 House animals and animals manure storage facilities used in agricultural field

Domestic  Combustion engines in vehicles  Old and badly designed landfills

 Improper disposal of municipal  Septic tanks and leakage from effluents of wastewater, sludge sewage systems and solid wastes

Industry  Atmospheric emissions from  Disposal of nitrogen-rich wastes energy production using well-injection techniques

 Combustion engines in vehicles  Old and badly designed industrial-

 Disposal of effluents by sludge waste landfills on fields

Introducing chemical fertilizers in the beginning of the last century has dramatically changed the patterns of nutrient flows in the landscape. The fertility of the crop fields is now totally independent of the surrounding land as the use of fertilizers made it possible to use even the nutrient-poor lands. Fertilizer levels increased, following the rapid intensification of agriculture, until the late 1980’s to application rates of over 350 kg-N/ha. The use of large amounts of inorganic fertilizer in agricultural practice resulted in considerable nutrient surpluses and consequently to considerable nutrient losses to the ground- and surface waters. Generally, enormous amounts of fertilizers are used all over the European and Mediterranean countries to ensure high agricultural productivity (EEA, 1999). Therefore, European and the Mediterranean soils are threatened by diffuse pollution from modern agriculture (Meinardi et al., 1995; WHO, 1998). Moreover, the contamination of surface water and groundwater with nitrogen is particularly severe in European agricultural environments (Meybeck and Helmer, 1989; Isermann, 1990). As an example, more than 52% of the groundwater aquifers in Germany are classified to be at risk or even polluted. Many farmers apply nitrogen as fertilizers or manures to their crops. Similarly, about 70% of the nitrogen pollution in the Netherlands originates from agriculture (Van Eerdt and Fong, 1998). The loss of nitrogen compounds from agricultural environments to the shallow groundwater and

44 surface water has increased dramatically in the Netherlands over the last decades (Oenema et al., 1997; Van Eerdt and Fong, 1998; Van Bruchem et al., 1999; Ondersteijn et al., 2002).

Consequently, the reduction of diffuse nitrate pollution from intensive agriculture is one of the main issues in the EU Water Framework Directive. The Nitrate Directive (1991) prescribes a - maximum concentration of 50 mg/L (as NO 3) in groundwater, which is equivalent to 11.3 mg/L (as Nitrogen). The EU Water Framework Directive has also made it obligatory that the EU countries have to address water pollution by nitrates from agriculture, according the EU Water Framework Directive, the member countries have also to implement an integrative management plan for the river basins including surface water bodies and groundwater aquifers.

3.4.3 Nitrogen Cycle and Factors Controlling Nitrate Processes in Subsurface

Knowing different paths and the fate of nitrate in the subsurface system as well as the factors that affect nitrate behaviour is a crucial requirement for groundwater nitrate pollution protection and remediation. Understanding the nitrogen cycle is also an important approach to study nitrate behaviour in subsurface system. Nitrogen is often the most important and determinant nutrient for both plant growth and crop yield. The dinitrogen molecule (N2) makes up about 79% of the extant atmosphere and is considered a stable element. Fixation is the process through which nitrogen leaves its inert gaseous status to enter the nitrogen cycle. Fixation can occur naturally either by the action of lightening or by means of specific groups of bacteria living in certain plant roots. Industrial fixation transforms nitrogen into nitrate or ammonia that occurs mainly in fertilizers production. Considerable amounts of energy (naturally or artificially) are needed to break the strong triple bond in both natural and industrial fixation of nitrogen (Sprent, 1987). Suitable energy occurs in the form of short light waves (photochemical reactions) and electric discharge (thunderstorms).

Biological fixation (Equation 3.1) is mediated through living organisms which transform the inert atmospheric nitrogen into reactive forms that can be utilized by plants. Biological fixation is usually accomplished by leguminous plant like soybean, alfalfa and clover. Biological fixation of nitrogen can be a major source of nitrogen in soil, especially in systems where legume crops are common in crop rotations (Graham and Vance, 2000; Chen, et al., 2003; Supeno and Kruus, 2002; Salvagiotti et al., 2008). Industrially, nitrogen combines with hydrogen under pressure to form ammonia in the well-known Haber-Bosch process. Free ammonia is afterward used or processed to ammonium salts or urea.

+ − N2 + 6 H + 6 e 2 NH3 …………… Eq. 3.1 (Hatfield and Follett, 2008)

45 Nitrogen could be also industrially fixed as a by-product of certain combustion processes (Sprent, 1987). Natural atmospheric deposition of nitrogen is generally between 10 and 50 kg/ha.year in the main agricultural regions (like Western Europe) and forms a potential source of nitrate in the subsurface system beside the anthropogenic activities that contribute in increasing nitrate amounts in the system. Therefore, the top soil and the lower unsaturated layers represent a reservoir for different nitrogen forms. Virgin soils contain as much as 6,000 to 10,000 pounds of organically-bonded nitrogen where agricultural soils are subjected to additional nitrate as a mean of fertilization. Once nitrogen is fixed, it is subjected to several chemical reactions which convert it to different organic or organic forms. Organic residues of livings contain nitrogen and represents about 98% of the total nitrogen storage in soil. This form is neither mobile nor available for plants. The other small portion exists in the forms of ammonium or nitrate. Nitrate in the soil layer is consumed either by plant roots or by some microorganisms as it is an integral part of the chlorophyll and a major constituent of enzymes (Stevenson and Cole, 1999).

Fig. 3.2: Nitrogen cycle (Hatfield and Follett, 2008).

46 Nevertheless, nitrate is still one of the most common groundwater contaminants. It is chemically stable, highly mobile and soluble and weakly attached to soil particles (Masetti et al., 2008; Toda et al., 2002). Therefore, nitrate in the groundwater is of great concern as it contaminates drinking water supply and consequently threatens human health and environment. Nitrate in the subsurface system could be transported with water, consumed by plants, biologically reduced, retarded or leached downward to the groundwater table where the same nitrate-related processes could also occur. The cycle of processes in which nitrate is a major part is known as nitrogen cycle and start with nitrogen fixation. These processes will directly or indirectly affect nitrate concentration in groundwater (Freez and Cherry, 1979; Ju et al., 2006).

Herein, the main processes that contribute to spatial as well as temporal variation of nitrate distribution in the subsurface system are summarized. This is attributed to the variation of seepage rate, water content, fertilization and cropping system, porous media properties, organic content, soil acidity, soil temperature and moisture content and thickness of the unsaturated zone (Mohanty and Kanwar 1994; Tindall et al., 1995; Santos et al., 1997; Townsend and Young, 2000; Astatkie et al., 2001; Porporato et al., 2003; Li et al., 2004; Onsoy et al., 2005; Ju et al., 2006; Li et al., 2007). One of these processes is known as mineralization or ammonification where ammonium is produced from natural decomposition of complex organic matter (Kim and Burger, 1997).

+ Organic nitrogen + microorganism NH3 | NH4 ……… Eq. 3.2 (Stadler, 2005)

The rate of mineralization depends on the nature of organic matter and is controlled by the need of microorganisms for carbon (Khalil et al., 2007). Some microorganisms will oxidize ammonium into nitrate in a process called nitrification. Mineralization as well as nitrification depends on pH value, soil moisture and temperature (Duwig et al., 2003; Cabrera et al., 2005). Nitrogen mineralization is positively correlated to the amount of nitrogen and negatively associated to the C/N ration; mineralization reaches its maximum rates in rainy seasons (Usman et al., 2000; Ste-Marie and Pare, 1999; Hacin et al., 2001). Moreover, nitrification is linked to the biological activity, whereas mineralization could be stimulated through tillage (Calderon et al., 1999). Nitrification rate occurs at its optimal rates in a well- aerated and moist soil involving a range of temperature between 19oC and 30oC (Andrews et al., 1997; Hobara et al., 2001). The previously mentioned processes increase nitrate amounts in the subsurface system. Nitrosomonas and Nitrobacter are the two major species responsible for nitrifying ammonium to nitrate (see Equations 3.2 and 3.3).

− + − NH3 + O2 NO2 + 3H + 2e ………… Eq. 3.3 (Hatfield and Follett, 2008)

47 − − + − NO2 + H2O NO3 + 2H + 2e ………… Eq. 3.4 (Hatfield and Follett, 2008)

Where the simple overall euation which describe nitrification process could be written as:

+ - - NH4 + O2 (Nitrosomas) NO2 + O2 (Nitrobacter) NO3 … Eq. 3.5 (Stadler, 2005)

After stating the above-mentioned factors, it could be summarized that nitrate in unsaturated zone could be lost through denitrification, runoff, or volatilization (Ghosh and Bhat, 1998). If nitrate in the soil system is not carried by the run-off water, taken up by plants or microorganisms. The non-consumed nitrate that will not be sorbed or reduced will eventually leach to groundwater and represents a pollution source (Tilahun et al., 2004; Townsend and Young, 2000). Leaching process mainly depends on rainfall rates and intensity, irrigation practices, amount and time of applied fertilizers, soil water content and nature of soil. Managing nitrate leaching is a challenge as it should account for soil properties, land use, cropping system, and hydrological features (Kim and Burger, 1997; Riley et al., 2001; Delgado, 2002; Delgado and Follett, 2002; Meisinger and Delgado, 2002; Munoz et al., 2003; Flint et al., 2008).

Downward movement of nitrate (leaching) is controlled by many factors including soil properties, recharge rates and moisture content, evaporation, soil acidity, organic matter content, microbial growth, the cropping and fertigation system, oxygen availability and redox potential (Ottman and Pope 2000). Fertilizer management has also a significant influence on the amount of nitrate leaches from the root zone to the deeper layers (Riley et al., 2001; Paramasivam et al., 2002; Li et al., 2007), one of the real cases showed that high fertilization and irrigation rates in a cropped soil increase the accumulation of nitrate in the unsaturated zone and then the chance that some portions will leach (Costa et al., 2002). The seasonal nitrate leaching was at its maximum rates when fertigation was applied at the beginning of irrigation periods (Mantovi et al., 2006). However, when applying the nutrients at the end of the irrigation cycle, nitrate leaching potential could be significantly decreased (Luo et al., 2003; Gaerdenaes et al., 2005). Cropping is also a very important key activity in controlling nitrate concentrations in groundwater (Thorburn et al., 2003; Cinnirella et al., 2005). Soil texture and particle grain size will directly affect nitrate distribution (Umezawa et al., 2008). Sandy soils allow most nitrates to leach downward and according an experimental study (Tindall et al., 1995) the denitrified portion will not exceed 2% of the total available nitrate, while about 40% of the available nitrate will be retarded by clayey soils. Soil texture will affect nitrate transformation processes (Andrews et al., 1997), soils with more silt, clay and organic matter retard more nitrate than straight sand (Gaines and Gaines 1994). It was indicated that there is a significant downward movement of nitrate, and hence nitrate concentration at a

48 certain depth will decrease with time (Luo et al., 2003). Below the root zone and in the deep vadose, nitrate is affected mainly by the denitrification processes. This process seems to be limited and has minor significance in this zone because of the absence of suitable conditions (a sufficiently population of denitrifying bacteria, electron-donor (i.e. organic matter) and restricted availability of oxygen) (Onsoy et al., 2005). Downward movement of water, either rainfall or irrigation based, will stimulate nitrate downward leaching (Costa et al., 2002; Ju et al., 2006). It is proven that heavy rainfall events combined with a sandy nature of the soil will lead to a significant increase of nitrate in well waters (Saadi and Maslouhi, 2003). Advective transport seems to be the predominant mechanism of nitrate transport in sand soils (Santos et al., 1997). The vadose zone thickness is an important factor affecting the distribution of nitrate in the unsaturated zone. The thicker the unsaturated zone is, the less likelihood that the groundwater will be polluted by nitrate (Saadi and Maslouhi, 2003).

3.4.4 Nitrate Denitrification in Soil and Groundwater

Plants and microorganisms utilize nitrate for their growth in a process called assimilatory nitrate reduction. This process occurs mainly in the layer of plants roots and reduces nitrate form the subsurface system. Dissimilatory nitrate reduction is also a nitrogen-related process in which nitrate is reduced into gaseous nitrogen forms. The process takes place in anaerobic environments where nitrate is used instead of molecular oxygen as terminal electron acceptor (Blockle et al., 1984; Trundell et al., 1986; Sprent, 1987; Korom, 1992). Denitrification is significantly recognized for its role in reducing nitrate in soil and groundwater (Stevenson and Cole, 1999; Thayalakumaran et al., 2004). Biological denitrification is a microbially-mediated process which indicates the stepwise reduction of nitrate through nitrite, nitrogen oxide and nitrous oxide, ending with gaseous nitrogen (Reddy and Patrick, 1984; Tiedje, 1988; Korom, 1992; Kim and Burger, 1997; Jorgensen et al., 2004).

A number of environmental conditions are needed for denitrification to take place; anoxic conditions and presence of nitrate as an electron acceptor are prerequisites for denitrification (Korom, 1992). Soil-water saturation is also an important requirement for the microbial population to start this process (Hiscock et al., 1991). Denitrification is controlled by many different environmental factors like pH and temperature. The optimum pH for denitrification is in the range of 6 to 8, but considerable denitrification activity can be found at pH values up to 4 (Heinen, 2006; Wriedt and Rode, 2006). Denitrification rate increases at hot windy conditions (Perez et al., 2003; Rodriguez et al., 2005). According to different studies cited by Wendland and Kunkel (1999), oxygen concentration limiting denitrification in groundwater range between 1 and 5 mg/L. Denitrification usually occurs in almost all known environments. Nevertheless, it is particularly prevalent in sewage and heavily fertilized soils

49 where many plants and microorganisms consume nitrate, reduce it and incorporate it into organic matter. The less free oxygen the groundwater contains, the more effectively nitrate is degraded, and also the more the residence time in the aquifers, the greater the degradation is (Wendland et al., 1994). The main products of biological denitrification are N2O and N2, which represented for example about 20-40% losses of the applied fertilizers in a rice field in India, 30% - 50% in Japan, and about 37-68% in the United States (Ghosh and Bhat, 1998).

Evolution of nitrogen gases (N2 and N2O) is generally an indicator for denitrification occurrence. Nevertheless, lack of these gases does not necessarily mean the denitrification does not exist. This is because of the high water solubility of these gases. Denitrifiers are facultative anaerobic organisms because they can use oxygen for their respiration when it is present, and change to nitrate when conditions become anoxic (Knowles, 1982; Tiedje, 1988). Furthermore heterotrophic denitrification is an energy demanding process and the required energy is usually derived from oxidation of organic matter (Hiscock et al., 1991). In waterlogged soils anoxic conditions predominate because the chemical and microbial demand for oxygen greatly exceeds the supply and the solubility and diffusion of oxygen O2 in water is poor. Contrary to oxygen, the diffusion rate of nitrate will increase under waterlogged conditions (Reddy and Patrick, 1984). Chemodenitrification is the abiotic nitrate reduction process in which inorganic matter is used as energy source (Sierra-Alvarez, et al., 2007). Pyrite, in autotrophic denitrification, can be used as an electron donor leading to a reduction of nitrate and oxidation of sulphide (Blicher-Mathiesen and Hofmann, 1999). Chemodenitrification usually takes place in soils with low pH (ranges from 3 to 4) and high amount of ferrous iron in solution (Jha and Bose, 2005; Sabzali et al., 2006; Van Cleemput et al., 1976). The main end product of chemodenitrification is NO (Van Cleemput and Baert,

1984; Bremer, 1997), although N2O may also be formed (Chalk and Smith, 1983; Blicher- Mathiesen and Hoffmann, 1999; Matheson et al., 2003; Sabzali et al., 2006; Alvarez et al., 2007; Soares, 2002; Thayakumaran et al., 2004; Hecke et al., 1990). The following equations describe nitrate denitrification where equation 3.6 describes the heterotrophic denitrification, equations 3.7 and 3.8 describe the autotrophic reduction of nitrate through pyrite oxidation and equation 3.9 describe the autotrophic reduction by Fe(II) (Stadler, 2005):

- - 5 CH2O + 4 NO3 2 N2 + 4 HCO3 + CO2 + 3 H2O ……………….. Eq. 3.6

- + 2+ 2- 5 FeS2 + 14 NO3 + 4 H = 7 N2 + 5 Fe + 10 SO4 + 2 H2O ……………….. Eq. 3.7

2+ - + 10 Fe + 2 NO3 + 14 H2O = 10 FeOOH + N2 + 18 H ……………….. Eq. 3.8

2+ - + 10 Fe + 2 NO3 + 14 H2O = 10 FeOOH + N2 + 18 H ……………….. Eq. 3.9

50 It was shown that denitrification process in the shallow groundwater is not likely to occur as in the deeper waters (Almasri and Kaluarachchi, 2004). A field survey was conducted on the aquifer in Kansas, the results showed that nitrate concentrations in groundwater is affected by depth of water table; nitrate concentration decreases downwardly in the groundwater. Presence of clay lenses will retard or delay nitrate movement. Therefore, the retarded nitrate will be prone to denitrification or its direction will be changed (Townsend and Young, 2000). Through a study conducted in a coastal area in Japan, it was shown the nitrate concentration decreased notably with depth in the groundwater. This phenomenon was attributed to the occurrence of microbial denitrification (Toda et al., 2002). These results are consistent with a study on the groundwater in Kansas, USA. There are only few studies available that provide field’s measurement of reaction kinetics. Frind et al., (1990) found that the half-life constant - for a NO 3 for autotrophic denitrification ranges from 1.0 to 2.3 years. Molenat and Gascuel- Odoux (2002) reported a half-life constant of autotrophic denitrification in the range 2.1 to 7.9 days where complete heterotrophic denitrification just needs few hours based on investigation in Pyrite-rich schist aquifers. Estimates of annual N loss to denitrification ranged from less than 1 kg/ha.yr (as Nitrogen) in a well-drained sand soil to over 40 Kg/ha.yr (as - Nitrogen) in a poorly-drained clay loam soil. Lack of available NO 3 was the primary factor limiting denitrification in summer, but available carbon was probably occasionally limiting (Groffman and Tiedje, 1989). It was also found that the reduction of nitrate ranges from 20 to 100% and is depending on soil type and residence time as well as on the redox potential which ranges from 250 to 190 mV respectively (Wlodarczyk et al., 2003). Denitrification also depends on the depth of soil layer as well as on the amounts and types of fertilizers applied on the land surface in the agriculture fields. The top soil was the major layer for denitrification with losses ranging from 9 to 26 kg/ha.yr (as Nitrogen) in a fertilizers-free soil. Denitrification increased to about 13 to 49 kg/ha.yr (as Nitrogen) in where fertilizers are applied. However, the sub soils contributed 20% of the total denitrification loss. Denitrification losses in the sub soil when amended with fertilizers were two times higher than the losses in a non-amended soil (Koops et al., 1996).

3.4.5 Nitrate Sorption in Soil and Groundwater

Sorption is a mass-transfer process among solution phase and solid particles which results in concentration change of a certain pollutant in the subsurface system. All solid surfaces in soils and aquifers can act as adsorbers with a certain exchange capacity for cations and anions. The solid phase of soils includes both inorganic and organic components. Based on major factors affecting nutrient mobility, soils solid phase can be divided into two broad types: soils with permanent charges on the surface of their solid particles, and soils with

51 variable charges on the surface of their solid particles. The second type of soil is of great significance for anions mobility as this type of soil could carry positive charges on the solid surface. Therefore, the effectiveness of soil minerals to sorb anions, like nitrate, on their solid particles surface depends on different parameters: the charge characteristics of the soil, the chemical properties of the anions and reaction mechanisms with soil minerals. Variable charge soils are more common in tropical than in temperate zones because their formation is faster in warm and moist environments (Sollins et al., 1988). It has also been proved in many studies that transport of indifferent anions is retarded due to sorption on the surface of some solid particles in soils like highly-weathered soils, tropical and subtropical soils (Katuo et al., 1996; Bellinin et al., 1996). The significant importance of anion exchange capacity was investigated by (Black and Waring, 1979). Nitrate sorption has often been observed in many tropical and volcanic ash soils (Schalscha et al., 1974). It has also been found that nitrate movement is retarded due to sorption mechanism in different soils in Nigeria through the positive charge they develop (Wong et al., 1987).

The inorganic part of soil consists largely of alumina-silicates (oxides of aluminium and silicon with small amounts of metals ions). This inorganic part ranges from highly crystalline to amorphous with considering most of clay minerals to be weekly crystalline with high rates of isomorphous substitution. Two main sources of the charge in clay minerals are isomorphous substitution in the crystalline structure and dissociation of the H+ ions. Development of the negative charge on clay minerals is mainly due to isomorphous substitution. This is the substitution of one element for another in ionic crystals without change of the structure. It takes place during crystallization and is not subject to change afterwards. Therefore, it is called permanent charge and is independent of the ambient pH.

The pH-dependant charge is variable and may either be positive or negative. The broken bonds on some clay minerals are always governed by association-dissociation reactions depending on the pH of the soil compared with the pH value of the point of zero charge at which plus and minus charges balance each other. In allophanes, some clays (Kaolinite and the metal oxides) will tend to develop positive charges because of the protonation of the OH- group on the oxide surfaces at low pH value compared with the pH value at the zero point charge.

Adsorption of dissolved contaminants is very dependent on pH. As noted previously in the discussion of the pH of zero-point-of-charge, pHzpc, the magnitude and polarity of the net surface charge of a mineral will change with pH (Dobson et al., 1997; Stumm and Morgan,

1981). At pHzpc, the net charge of a surface changes from positive to negative. Mineral surfaces become increasingly more negatively charged as pH increases. At pH < pHZPC, the

52 surface becomes protonated, which results in a net positive charge and favors adsorption of contaminants present as dissolved anions. Because adsorption of anions is coupled with a release of OH- ions, anion adsorption is greatest at low pH and decreases with increasing pH. At pH > pHzpc, acidic dissociation of surface hydroxyl groups results in a net negative- charge which favours adsorption of contaminants present as dissolved cations. Because adsorption of cations is coupled with a release of H+ ions, cation adsorption is greatest at high pH and decreases with deceasing pH.

Nitrate adsorption is found to be highly dependent on the pH value and nitrate concentration. Increasing of pH and leaching nitrate concentration will reduce nitrate sorption capacity and its retardation in soils (Wang et al., 1987; Wong et al., 1990; Katuo et al., 1996; Bellinin et al., 1996; Eick et al., 1999; Qafoku et al., 2000; Strahm and Harrison, 2006; Black and Waring, 1976). Soil mineralogy and surface charge also affect anions sorption (Sumner and Davitz, 1965; Uehara and Gillmann, 1980; Qafoku and Sumner, 2001, Qafoku and Sumner, 2002). High chloride, phosphate and sulphate content in soil will also reduce nitrate sorption due to competition of exchange sites (Eick et al., 1999; Strahm and Harrison, 2006). Katou et al., (1996) found that nitrate ion has smaller affinity to the adsorption sites than chloride but higher than sulphate. He concluded that the adsorption of monovalent anions was largely due to the increase in the anion exchange capacity of the soil in response to an increase in the ionic strength of the bulk solution (Singh and Kanehiro, 1969; Katou, 2004). Potential places for anions sorption, like nitrate, are the reactive sites on surfaces of some soils: hydroxyl sites on the Al-Oxides and the Fe-Oxides surfaces and on the Kaolinite edges (AlOH and SiOH) (Qafoku and Sumner, 2002). Nitrate sorption in Andisols samples was - estimated to be at least 300 Kg/ha (as NO 3-N) in about 4 m thick layer. Nitrate adsorption is also a long term process which means that the period over which nitrate adsorption lasts could extend to about 10 years (Ryan et al., 2001; Maeda et al., 2007).

There are two main techniques used to assess sorption isotherms: batch experiments and miscible-displacement techniques. Miscible displacement is also known as flow-through techniques. They are probably the most reliable method for obtaining sorption parameters. The flow-through techniques posses a number of advantages over the traditional batch methods in that they are mainly based on simulating the field conditions through maintaining narrow soil-solution ratios and by allowing the soil phase to be at rest (Sparks, 1982; Miller et al., 1989; Bond and Philips, 1990; Igler et al., 1998; Maraqa, 2001). The equilibrium method tend to measure more nitrate sorption, it has been found that nitrate sorption in silty soils and using batch experiment is about 30% of the total available nitrate in the solution (Kowalenko

53 and Yu, 1995). It has also been estimated that nitrate transport in soil was about 25% - 40% slower than bromide transport (Clay et al., 2004).

3.4.6 Nitrate Retardation and Leaching

Retardation factor empirically describes the rate of contaminant transport relative to that of recharge water or groundwater. Retardation factor is usually formulated through the partition coefficient which is defined as the ratio of the contaminant concentration associated with the solid to the contaminant concentration in the surrounding aqueous solution when the system is at equilibrium. Retardation factor could be estimated based on the partition coefficient and some other soil properties like bulk density and volumetric water content (Wild, 1981). The partition coefficient is a measured parameter that is obtained from experiments including laboratory batch experiments, in-situ batch method, and laboratory flow-through (or column) method. There are different methods used to measure the partition coefficient values. Each method has advantages and disadvantages, and perhaps more importantly, each method has its own assumptions. Leaching is also a significant term in groundwater pollution investigations. This is because solutes that will not be retarded through certain mechanisms will eventually be leached to the groundwater aquifer.

Because of its high solubility in water and low affinity to be sorbed on the permanent-charge soils, nitrate is thus vulnerable to being washed out of the soil system by percolating water. Nitrate leaching after fertilization has a potential to pollute the underlying groundwater system (Flint et al., 2008) Field investigations were carried out in adjacent forest and heathland ecosystems in Northwest Germany and considerable amounts of nitrate were measured leaching from the top soil layers (Herrmann et al., 2005). In humid regions, nitrate leaching is an unavoidable consequence of fertilizers application. The leaching rate is much higher in sand soils and under over fertilization (Köhler et al., 2006).

The risk of groundwater pollution with the leaching nitrate could be reduced through different management actions; fertilizers management and choosing an optimum irrigation system are two major examples which could reduce nitrate leaching to aquifers (Spalding et al., 2001). Leaching rates were also reduced through enhancing denitrification capacity which was achieved through organic and integrated fertilization practices (Kramer et al., 2006). Three different numerical models were applied to assess nitrate leaching in the forest area in the Netherlands. All three models indicated that the EU standard for nitrate concentration in drinking water (50 mg/L) was already exceeded because of the high leaching rate of nitrate (Kros et al., 2004). Downward leaching of nitrate is, like other nitrate processes, a function of ionic strength of the soil-water solution, soil type and properties, profile depth, cropping and

54 fertilizers system, and irrigation and rainfall rates (Luo et al., 2003; Al-Darby and Abdel- Nasser, 2006). Fly amendment is also one of the potential alternatives to reduce nitrate leaching from sandy soils. Ash-modified sandy soils had a higher sorption capacity for nitrate and lower leaching potential of about 20% (Pathan et al., 2002).

55 4. DRASTIC: APPROACH & RESULTS

4.1 Introduction

This research aims to modify DRASTIC method for a more-representative and quantitative evaluation of groundwater vulnerability to nitrate contamination in a part of the Venlo Block in the North Rhine-Westphalia. As shown in Figure 4.1, the main approach of this research is to integrate nitrate retardation parameter within the standard DRASTIC index. Firstly, the standard DRASTIC vulnerability index will be evaluated and assessed. Secondly, nitrate retardation is going to be evaluated through the Advection-Diffusion cell which will be validated using the analytical solution of the convention-dispersive transport equation. This chapter will discuss the first part of the approach, (DRASTIC method to evaluate the intrinsic groundwater vulnerability), and the related results. The second part of the approach is the Advection-Diffusion cell, which was utilized to estimate capacities of different soils to sorb as well as to retard nitrate is going to be discussed in the next chapter. The concept of coupling between the standard DRASTIC index and nitrate retardation and its results are presented in Chapter 6.

Fig. 4.1: The main methodology used in this study; DRASTIC and the Advection-Diffusion Cell.

56 4.2 Description of the DRASTIC Index

The DRASTIC method was developed by Aller et al., (1987) and is probably the most popular and widely used point count system model (PCSM) applied to assess groundwater vulnerability. It is simple and direct, and has been utilized to several groundwater basins in many regions in the world (Durnford et al., 1990; Rosen, 1994; Lobo-Ferreira and Oliveira, 1997; Lynch et al., 1997; Melloul and Collin, 1998; Burkart et al., 1999; Johansson et al., 1999; Kim and Hamm, 1999; Rupert, 2001; Al-Zabet, 2002; Baalousha, 2006; Stigter et al., 2006; Debernardi et al., 2007; Pathak et al., 2008; Wen et al., 2008). Conceptually, DRASTIC is based on grouping seven hydrogeological parameters thought to contribute in protecting groundwater from being polluted. As shown in Figure 4.2, the name DRASTIC stands for these hydrogeological parameters: Depth to groundwater, Recharge rate, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity of the aquifer. A hydrogeological description of the seven parameters incorporated in the DRASTIC index is briefed in Table 4.1. DRASTIC has been used to develop maps at a variety of scales, including national, (Lynch et al., 1994; Kellogg et al., 1997), state-wide (Seelig, 1994; Hamerlinck and Ameson, 1998), and individual counties and townships (US-NRC, 1993; Shukla et al., 2000). This index method is a very widespread-used approach in groundwater vulnerability assessment. The reason is that it is relatively inexpensive, straightforward, uses data that are commonly available or estimated, and produces an end product that is easily interpreted and incorporated into the decision-making process. Nonetheless, several studies have adjusted the weights of DRASTIC or even incorporated additional or alternative parameters into the index (Halliday and Wolfe, 1991; Ben-Kabbour et al., 2006; Guo et al., 2007; Mendoza and Barmen, 2006; Panagopoulos et al., 2006; Denny et al., 2007; Herlinger- Jr and Viero, 2007; Wang et al., 2007). The DRASTIC index is based on four major assumptions: contaminants are induced above at the ground surface; contaminants are flushed into the aquifer by precipitation; the contaminants have mobility of water; and the study area is not smaller than 4 km2 (US-NRC, 1993).

As stated above and as shown in Figure 4.3, DRASTIC has seven hydrogeological parameters. Each of those parameters is assigned a score (a weight) ranging from one to five. This score reflects the degree of protection provided by this parameter compared to the other six parameters. Afterwards, each individual parameter of the DRASTIC index is classified into ranges or into significant media types which have an impact on pollution potential. Each range (or media types) is assigned a rate (ranges from 1 to 10) to reflect protection degree. The rate 1 means a very low contamination potential or a very high protection degree. Considering that DRASTIC index is a numerical rating scheme based on

57 multiplying each factor weight by its point rating, a thematic map for each DRASTIC parameter is prepared with the utilization of the Geographical Information System (ArcGIS 9.3) (ESRI, 2009). Finally, DRASTIC index overlays and superimposes the product of weights and rates for the seven parameters in a certain groundwater basin according to the following formula.

DRASTIC Vulnerability Index

(DVI)  Dw Dr  R w R r  A w Ar  S wSr  T w Tr  I w Ir  C w Cr w : represents weight assigned for each parameter; r : represents ratings point system assigned to the different ranges (classes) in each of the DRASTIC parameters (see Fig. 4.3).

Fig. 4.2: Flowchart of groundwater vulnerability assessment based on DRASTIC method

58 Table 4.1: Description of the hydrogeological parameters used to assess the relative degree of groundwater vulnerability using DRASTIC (modified after: Aller et al., 1987)

Parameter Description

D Depth to groundwater is expressed also as the unsaturated zone thickness. It acts a natural protecting cover for the groundwater system from pollution and serves as a media where most of the natural attenuation processes occur

R Recharge rates indicates how much water infiltrate into the subsurface system. Recharge water is the main source of groundwater and represents contaminants carrier. Greater potential for pollutants to move occurs in areas with higher recharge rates. However, recharge may also dilute contaminants when its rate is high enough

A Aquifer media represents routes or pathways which are of great importance for both water and contaminants transport. They determine the travel time, attenuation potential and hence the protection degree provided by the aquifer media

S Soil media has a significant impact on the amount of recharge water that can infiltrate to the water table whether it is fine grained or course grained. In turn, the soil media itself has also great impact on contaminant transport. Fine grained materials, like clays and silts, decrease relative soil permeability and restrict contaminant migration. In addition, thick and thin soil layers also have an effect on the attenuation of contaminants

T Topography is considered as the slope of land surface and its variation. At steep slopes, the area tends to be more potential for pollutant runoff and therefore little pollutant retention and in turn little contaminants infiltration. On the other hand, shallow slopes have more potential for pollutant retention and in turn infiltration of contaminants

I Impact of vadose zone is the impact of the layer of sediments above the groundwater table which is unsaturated or intermittently saturated. The type of material that is present in the vadose zone determines the attenuation characteristics, length, path, the time available for attenuation and quantity of material that is able to come in contact with. The impact of the vadose zone incorporates two different features, the depth to water table and the soil permeability

C Hydraulic conductivity is a controlling factor of the rate at which water can move through permeable media under a given hydraulic gradient. It is controlled by the amount of pore space present in the soil media. Groundwater velocities also determine the rate at which water enters the aquifer

59

Fig. 4.3: Ranges, ratings and weight value of the DRASTIC parameters (modified after: Aller et al., 1987)

60 4.3 Groundwater Vulnerability Assessment in the Study Area

4.3.1 Data Collection

The necessary data for vulnerability evaluation were obtained from previous researches and literature resources. Some data were also obtained from the Geological Survey in the NRW (GD), NRW State Environmental Agency (LVERMA) and NRW State Survey Office (LANUV). We have used the Geographic Information System (ArcGIS 9.3) as a tool to handle these amounts of spatially-distributed data. The powerful processing capacity to analyse and manipulate data in spatial and tabular forms makes ArcGIS more usable than traditional tabular data systems. ArcGIS has a good capability to combine database management system with digital mapping (Hedges and Charnock, 1999; Lasserre et al., 1999; Babiker et al., 2007). Statistical analysis were performed on the data as well as on the results using the Statistical Package for the Social Sciences (SPSS, Version 15.0) (SPSS Inc., 2009). As shown in Figure 4.2, a thematic map for each parameter has been processed and prepared. Each map was represented by a raster grid from which the rating and weighting systems for each parameter were also conducted according to Figure 4.3.

4.3.2 Description and Evaluation of DRASTIC Parameters

The depth to groundwater table is the distance from the ground surface to the groundwater table in the unconfined aquifer. Depth to groundwater is also expressed as the unsaturated zone thickness. It acts as a natural cover protecting the groundwater system from pollution and serves as a media where most of the natural attenuation processes occur. Depth to groundwater table affects the time required for a contaminant to reach the aquifer. Contaminants will undergo chemical and biological reactions (dispersion, oxidation, natural attenuation, sorption, etc.) through their travel from the land surface to the groundwater table. Thin unsaturated zones (usually less than 1 m) do not provide delay or attenuation for pollutants in the same degree as in thick unsaturated zones. The delay and attenuation of groundwater recharge is increased as the thickness of the unsaturated zone increases. This is because thickness unsaturated zones mean long travel time and more contact between pollutants and soil minerals and organic matter and hence a higher possibility for delay and attenuation.

This parameter has been extracted according to Leppig (2004). The area is characterized by shallow groundwater where depth to groundwater varies from the ground surface (around the surface water bodies) to about 30 meters depth. A thematic map of the vadose zone thickness was also prepared for the study area. The unsaturated zone thickness has been

61 classified, weighed and rated. As shown in Figure 4.8, the thickness of the unsaturated zone in the study area has an average value of about 8 m whereas most of the study area (about 40%) has an unsaturated zone of a thickness in the range between (9 and 15 m). Around surface water bodies, the unsaturated zone thickness decreases in the study area to less than one meter, which means a high degree of hydraulic connection between groundwater and surface water and therefore, a high potential for groundwater pollution. More than 75% of the study area has a deep aquifer with a thickness of the vadose zone greater than 10 m.

Net Recharge represents the amount of water that penetrates the ground surface, infiltrates through the unsaturated zone and reaches the groundwater table. Recharge is the main source of groundwater replenishment. This recharge water is available for vertical transport of a contaminant to the water table and horizontally within the aquifer. In addition, it controls the volume of water available for dispersion and the degree of contaminants dilution in the vadose and saturated zones. Generally, the higher the recharge rate is, the greater the potential for groundwater pollution. However, recharge water may also play a very important role in dilution processes. Therefore, a scientific judgment should be found to alleviate between the role of recharge water as a pollutants carrier and its role as a dilution agent.

Groundwater recharge rates in the study area have been derived from the Geological Survey (NRW-GD, 2000). Recharge rates are based on an average value for the annual recharge rates for the period from 1961 to 1990. Generally, the area is characterized with a relatively high annual rainfall (about 750 mm/year). As shown in Figure 4.4, low recharge rates are associated in the study area with the urban areas because roofs and pavements will prevent the downward penetration of rainwater and will facilitate runoff. Low recharge rates occur also in the forest areas which could be attributed the high rates of evapotranspiration. More than 60% of the study area has a relatively large groundwater recharge rates (greater than 170 mm/year) (NRW-GD, 2000). There are small parts of the study area (around surface water bodies) where negative groundwater recharge was recorded. This is attributed to the evaporation process form these areas. As shown in Figure 4.4, the highest rates of recharge occur in agricultural areas. Moreover, the highest groundwater recharge values occur in the areas covered with sandy soils (Fig. 4.5) which is attributed to the nature of sandy soil which allow small amounts of runoff and the recharge water will easily move downward.

Aquifer media represents the available pathways which are of great importance for both water and contaminants transport. These pathways determine the travel time and the attenuation potential such as sorption, reactivity, and dispersion. The aquifer medium also influences the amount of effective surface area of materials with which the contaminant may come in contact within the aquifer. The paths which a contaminant will take can be strongly

62 influenced by fracturing, porosity, or by an interconnected series of openings which may provide preferential pathways for groundwater flow. Aquifer media in the study area was classified according to the hydrogeological maps (scale 1:25,000). These maps were prepared at the Department of Engineering Geology and Hydrogeology (LIH) at the RWTH Aachen University.

Fig. 4.4: Distribution of recharge rates over the different land use categories

Fig. 4.5: Distribution of recharge rates over the different soil classes

63 As a part of the Lower Rhine Basin, the study area is geologically characterized with Tertiary and Quaternary sedimentations. About 80% of the study area is occupied by a highly conductive groundwater aquifer which is composed mainly of sand and gravel. This will give the area a high rating value in evaluating the groundwater vulnerability as sand and gravel have relatively low protection function compared with some other fine materials. However, this large portion which covered with one type (rate) of aquifer media will decrease the significant of the use of this parameter (aquifer media) in DRASTIC index.

Soil has a significant impact on both water flow and contaminant transport. Fine grained materials, like clays and silts, have relatively low soil permeability and restrict contaminant migration. The negative functional groups in fine soils tend to adsorb the positively-charged ions. The texture or size of the soil particles will influence the rate at which contaminants percolate downward through the soil profile. Coarse-textured soils allow contaminants to pass faster through open spaces than in fine-textured silts and clays. Similarly, the greater the organic content of certain soil constituents, the greater the likelihood for attenuating reactions. For instance, organic material, clays, and other minerals can react with contaminants to degrade, adsorb, or volatilize the chemicals. Certain clays having large shrink and swell capacities will crack after drying, allowing contaminants to move unhindered through parts of the profile.

The GD-NRW prepared soil maps in a scale 1:25000. These maps; however, could not be directly used to produce soil classes to be afterward used in DRASTIC analysis and therefore have been transformed into texture-based soil maps (as shown in Fig. 4.6). The latest were used as the S-parameter in the DRASTIC index. Figure 4.7 and Figure 4.8 show the percentage of the five different types of soil within the study area. Sandy soil is a minor soil in the study area and occupies less than 5% of the whole area. Organic soil occupies also a smaller area than the sandy soils and is limited to areas around the surface water bodies. The major soil classes exist in the study area are silty sand, sandy silt and clayey silt. These classes are distributed over the study area in a way that makes this parameter (Soil media) of great significance and sensitivity in assessing the DRASTIC index.

Topography is represented in DRASTIC by the slope of the land surface. At steep slopes, contaminants tend to move with the runoff water and therefore little pollutant retention and in turn little infiltration of contaminants will take place. On the other hand, shallow slopes have more potential for pollutant retention and in turn infiltration of contaminants. Low slope means higher DRASTIC rating where contaminants are less likely to be released with runoff and therefore are more likely ready to infiltrate into the aquifer. Slope data is always available from the Digital Elevation Model (DEM). A 25-meter digital elevation model of the study area

64 was used to calculate the percentage slope. The slope is then classified according to the ranges criteria in Table 4.1 via the ArcGIS 9.3. The topography map displayed a gentle slope (less than 10%) over the most of the study area (see Fig. 4.7 and Fig. 4.8). The slope dramatically increases in the northern-eastern part of the study area around the Viersen Heights.

The unsaturated zone consists of pores which are partially filled with water. This zone includes the top soil layer, the intermediate aeration zone, and the capillary fringe; it is boarded by the phreatic groundwater table. The composition and extent of the unsaturated zone overlying an aquifer has an important bearing on the rate at which liquids and pollutants percolate from the ground surface to the aquifer. The vadose zone plays a significant buffering role between the ground surface and the groundwater table, as far as percolating water and pollutants are concerned. The unsaturated zone strongly influences attenuation, routing, and time-of-arrival of percolating liquids and pollutants moving through the unsaturated zone towards the water table. Physical and chemical attenuation processes can be active in this zone above the water table. They can include biodegradation, chemical reactions, dispersion, mechanical filtration, neutralization, and volatilization. An accurate assessment of the various areas and parameters of the vadose zone is therefore an important step for hydrological characterization of the media overlying any aquifer, especially when assessing groundwater vulnerability.

From the upper discussion and as shown in Figure 4.8, it is important to mention that the variation in the three parameters - Aquifer media, Topography, and hydraulic Conductivity - seem to have small contribution to the change and variation of DRASTIC index over the entire study area. This is because these parameters have single sub-classes covering more than 75% of the study area. This finding will be discussed in more details in Section 4.3.4 of sensitivity analysis of DRASTIC index.

65

GD, 2002). GD, - based soil map of the study area (modified after NRW area (modified ofstudy the basedmap soil - A texture A

: 6 Fig. 4.

66

oil media S quifer media, and (d) and quifer media, A echarge rates, (c) echarge rates, (c) R Maps of the different ratings of the DRASTIC parameters (a) Depth to groundwater table, (b) (b) table, groundwater to (a) Depth parameters the of DRASTIC different ratings the of Maps

: Fig. 4.7

67

gs of the DRASTIC parameters (e) Topography, (f) Impact of Impact (f) Topography, (e) parameters the DRASTIC of gs e and (g) hydraulic Conductivity and e(g) Maps of the different different ratin the of Maps : : vadose zon vadose (continue) Fig. 4.7

68

Fig. 4.8: Distribution of DRASTIC parameters, their sub-classes, and their rating values.

69 4.3.3 Evaluation of DRASTIC Vulnerability Index

Creation and manipulation of data elements including design and building functions are closely associated with data collection process and GIS application. A comperhensive GIS application aims to integrate model elements for data analysis, evaluation, illustration and display. As dispayed in Figure 4.8, creating databases for representation within the GIS is accomplished by compiling data from several dissimilar sources into a common format or by editing GIS point and vector attribute tables to reflect DRASTIC rating and weight assignments. Manipulation of individual DRASTIC index elements produces the integrated model that is a main focus of this research. To create this integrated model, databases undergo various transformations. The GIS tools perform the geoprocessing operations required for the production of layers that represent the seven model elements of DRASTIC.

Spatial reference for all data elements was defined to be the Germany Zone 2. Conversion of vector-formatted data to raster-format data is necessary to integrating the different elements of the DRASTIC model within the GIS. The data layers representing each DRASTIC element will be combined using the raster calculator within the ArcGIS Spatial Analyst extension. The resulting raster file is the layer used to evaluate the final groundwater vulnerability DRASTIC index. This layer is computed applying a linear combination of all factors.

Fig. 4.9: Methodological approach of the DRASTIC vulnerability assessment

70

(Based on the standard DRASTIC index) DRASTIC onthe standard (Based Vulnerability Map Vulnerability - inal Groundwater DRASTIC Groundwater inal The f The

: 4.10 Fig.

71 As depicted in Figure 4.10, the intrinsic DRASTIC vulnerability values range between 89 and 233. DRASTIC index in the study area was classified into four different vulnerability groups. These classes are: “low vulnerability” which ranges from 50 to 100, “moderate vulnerability” which ranges from 100 to 150, “high vulnerability” which ranges from 150 to 200 and “very high vulnerability” which ranges from 200 to 250 (see Fig. 4.10 and Fig. 4.11). The DRASTIC map clearly shows the dominance of the “high” and “very high” vulnerability around the surface water bodies and within the agricultural fields. This is actually due to the high pollution risks associated with the wide range of agricultural applications as, for example, the use of fertilizers and pesticides. The “low” and the “moderate” vulnerability classes are concentrated around the Viersen Heights area because of the steep slope of the land surface in addition to the presence of clayey soils.

Fig. 4.11: Statistical overview of the DRASTIC index over the entire study.

Figure 4.11 elucidates DRASTIC values according to the occupied areas. Statistically, the average value of the DRASTIC index over the entire study area is about 180, which is approximately the same value of the median (percentile 50%). This average value is located within the class “high” vulnerability. The low values of the DRASTIC vulnerability index (lower than 150) have a small frequency where the values around the average have much higher frequencies. Figure 4.12 shows that a very small part of the study area (about 8%) is classified to have very low groundwater vulnerability, where medium groundwater vulnerability is found in less than 10% of the area. The majority of the area (more than 85%) has high to very high groundwater vulnerability.

72

Fig. 4.12: Statistical overview of the DRASTIC vulnerability index over the entire study area

4.3.4 Sensitivity Analysis of the DRASTIC Index

The direct vulnerability assessment using variety of different hydrogeological attributes is a main feature of DRASTIC (Aller et al., 1987). It was also found that the seven parameters of the DRASTIC index were quite independent and therefore, representative enough to assess groundwater vulnerability (Rosen, 1994). Moreover, the use of such high number of data layers reduces the uncertainty of the final DRASTIC map (Evans and Myers 1990). Nevertheless, some studies have produced more representative vulnerability maps through different modification of the standard DRASTIC index. There are also some critiques and arguments for parameters’ redundancy, high subjectivity and the disability of DRASTIC index in assessing groundwater vulnerability (Barber et al., 1993; Merchant, 1994). Sensitivity analysis of the DRASTIC vulnerability aims at evaluating the relative significance of DRASTIC parameters, and their influence on the resultant maps. Two approaches are available to analysis the sensitivity of DRASTIC; map removal (Lodwick et al., 1990) and single parameters sensitivity (Napolitano and Fabbri, 1996).

(a) Map removal sensitivity

This type of sensitivity analysis evaluates weather it was really necessary to use all the seven parameters. This is achieved through characterizing the influence of each single parameter on the final vulnerability map. The sensitivity of the standard vulnerability map toward removing one or more of the DRASTIC parameters and is computed using the following formula (Lodwick et al., 1990):

S = (| 7/V - V ' |)n/ x ( 100 )V/ ,

Where S is the sensitivity variation index, V and V’ are the unperturbed and the perturbed vulnerability indices respectively, and n is the number of the parameters used to estimate the

73 new vulnerability index after deleting one or more layers. The actual vulnerability map (V) is obtained using the standard DRASTIC index.

(b) Single parameter sensitivity

The single parameter sensitivity analysis is computed to measure the impact of each parameter on the standard DRASTIC index. This type of sensitivity analysis is a comparison between the theoretical weights given into the standard DRASTIC index and the effective or the real weights for each parameter. The effective weight is computed from the following formula (Napolitano and Fabbri, 1996):

EW = (Ri x Wi / V) x 100 ,

Where EW is the effective weight, R and W are the rating value and the weight of each of the DRASTIC parameters respectively, and V is the vulnerability index computed using the standard DRASTIC method. Assessment of sensitivity analysis deals with a large number of data and layers. This requires a well-structured data base and the utilization of GIS.

The results of sensitivity analysis are presented in Figure 4.13 and Figure 4.14. They summarize the variation of the vulnerability index as a result of removing only one parameter at a time as well as the effective weights of DRASTIC parameters. As can be inferred from Figure 4.13, it is difficult to pick out a clear statement about the sensitivity of DRASTIC index to the removal of its standard parameters. Considering the means of the individual parameters, the vulnerability index seems to be have closed sensitivity index most of these parameters. The effective weights of the DRASTIC parameters exhibited some deviation from their theoretical weights as summarized in Table 4.2. Soil texture, depth to groundwater table and the impact of vadose zone (the protective layer) tend to be the most effective parameters in the vulnerability assessment (mean effective weight is 21.5%) while the hydraulic conductivity and aquifer media are considered to have lower significance in term of effective weights. Additionally and as discussed previously, these four parameters have relatively large variation over the entire study area compared with the aquifer media and hydraulic conductivity and therefore, these four parameters are more sensitive and have significant impact on the change of the final assessed DRASTIC index.

74

Fig. 4.13: Map removal sensitivity analysis for the DRASTIC parameters.

Fig. 4.14: Effective weight sensitivity analysis of DRASTIC parameters.

4.3.5 Correlation of DRASTIC Vulnerability with Nitrate & Land Use

The DRASTIC vulnerability mapping method was improved after being modified in many other studies in different locations in the world. It has also been modified based on correlation with land use categories, soil characteristics and other additional hydrogeological parameters (Mendoza and Barmen, 2006; Panagopoulos et al., 2006; Denny et al., 2007; Herlinger-Jr and Viero, 2007). The DRASTIC index was also calibrated using nitrate concentraion in groundwater on the basis of statistical correlations (Rupert, 1998). Beside assessing the DRASTIC vulnerability groundwater, a main objective of this research is to

75 understand if the DRASTIC index outputs do represent the actual status of groundwater nitrate contamination. Nitrate has been chosen because it is a good indicator for groundwater quality and its data is always availble. Understanding the DRASTIC map is achieved through analysing the vulnerability map, correlating it with the existing land use, and the actual measured patterns of nitrate data. This study will combine different approaches of analysis. As a major objective of this study and based on this analysis of DRASTIC appropriateness, which will be discussed afterward, the DRASTIC index will be modified after considering the capacity of different soils to sorb as well as retard nitrate.

. Spatial and Temporal Distribution of Nitrate in Groundwater

Groundwater sampling is a time-consuming and costly process. It is always limited by climate conditions, social acceptance and natural hydrogeological conditions. This makes nitrate data limited to some points which will restrain nitrate identification over the entire groundwater system. Data related to nitrate concentration in groundwater in the period extending from 1986 to 2006 were compiled for this study. These data sets are for wells in the shallow unconfined aquifer, and have been obtained from the same sources as for the hydrogeological data sets. The data, in its point form, has been analyzed and the basic statistical parameters have been calculated. A clear increasing trend in nitrate concentration in groundwater is found over the time as shown in Figure 4.15. The average nitrate concentration in groundwater for the period (1986-1992) was about 90 mg/L which is greater than the maximum allowable limit. The curve also shows a decreasing trend between the period (1993-1999) and the period (2000-2006). This decrease could be attributed to the possible management action taken by the responsible institutes.

Geostatistics is widely applied in groundwater management as it has the capability to analyze spatial and temporal variability of data (Rossi et al. 1992; Hossain et al. 2007; Mardikis et al. 2005; Basistha et al. 2008). This analysis accounts for location and time which are crucial to understand both spatial and temporal patterns, and infer values of nitrate concentration at the unsampled locations (Delhomme, 1979; de Marsily, 1986; Dagan, 1985; Cain et al., 1989; Goovaerts, 1997; Deverel, 1989). Kriging is one of the available interpolation tools (Rizzo and Doughetry, 1994), which assumes that nitrate concentrations at closed location are likely to be the same or at least similar (Olea, 1991; Goovaerts, 1997). Ordinary kriging has been applied in order to generate surface maps for nitrate concentrations in groundwater in the study area. The map in Figure 4.16 represents an interpolated surface map of the average values of for 142 sampled points of nitrate concentration in groundwater over the years from 2000 and 2006.

76

n = 142

Fig. 4.15: Normal distribution of nitrate concentration in the upper unconfined aquifer for three different periods.

. Correlation between DRASTIC Index and Nitrate in Groundwater

Afterward, the final DRASTIC vulnerability index has been correlated with the generated surface map of average nitrate concentration in the time period from the year 2000 to the year 2006. Correlation results between nitrate concentration map in the groundwater shows that nitrate concentration has – relatively - the best correlation to both hydraulic conductivity and aquifer media (coefficient of correlation r = 0.484) followed by depth to water table parameter (r = 0.413) which is also coincide with the correlation results between DRASTIC and its parameters. A weak correlation was found between nitrate concentrations and the impact of the vadose zone and the recharge rate (r = 0.065 and 0.121 respectively). It was also found that nitrate concentrations were relatively poorly correlated to the slope of the land surface (r = 0.238). This shows that most effective parameters on groundwater pollution with nitrate in the study area are the depth to water table and hydraulic conductivity respectively.

However, this shows that some important parameters are not well represented by DRASTIC, especially after proving that DRASTIC index is highly sensitive to these parameters (as discussed in the section of sensitivity analysis). On the other hand, these parameters are very important in controlling solutes transport and therefore, should be properly considered. Soil, for example, control nitrate transport and transformation processes and its contribution in groundwater protection should be properly reflected in DRASTIC vulnerability assessment.

77

Nitrate concentration in groundwater (average value for the years from 2000 to 2006).to from 2000 the years for value (average groundwater in concentration Nitrate

16 Fig. 4.

78 Figure 4.17 shows the kriging variogram which indicates to which extent are sampled points of nitrate concentration in groundwater are spatially correlated. The variance is plotted on the y-axis and the distance between the possible pairs of points is plotted on the x-axis.

Variance (10-4*m2)

4.08

3.26

2.45

1.63

0 0.28 0.56 0.84 1.13 1.41 1.69 Distance (km) Fig. 4.17: Kriging variance of 142 sampled points of nitrate concentration

As shown in Figure 4.18, the results indicate the presence of correlation between nitrate concentration in groundwater and the final DRASTIC index (r = 0.539). This means that a non-strong correlation between the DRASTIC vulnerability index and the actual nitrate concentration in the upper shallow aquifer should exist. However, this value of correlation is not high enough for a scientific judgment. If the DRASTIC vulnerability index will be used in the decision making process for nitrate pollution related aspects, the assessed vulnerability index should represent nitrate status on the groundwater system.

. Correlation between DRASTIC Index and Land Use

Land use is also a main factor in controlling groundwater contamination as land activities are always a main source of pollutants. Use of inorganic and organic fertilizers, landfills and improper disposal of wastewater are different forms for land use which are possible sources of nitrate in groundwater. Therefore, excessive nitrate concentrations in groundwater are related to land use activities where agriculture is the main source of nitrate in the groundwater within the study (NRW-MUNLV, 1995b). However, nitrate is a highly mobile and low reactive pollutant.

79

Fig. 4.18: Nitrate distribution over the different DRASTIC classes

Fig. 4.19: Nitrate distribution over the different land use categories in the study area

80

Fig. 4.20: Distribution of the final DRASTIC vulnerability index over the different land use categories in the study area

This means that nitrate applied at a certain location on the land surface at a certain initial time could be easily found, after considerable period of time, in completely another location in the groundwater. Distribution of nitrate concentration over the different land use classes in the study area is presented in Figure 4.16. It is clear that high values of nitrate are found around surface water bodies and are concentrated in the agricultural input-controlled areas, the fact which is also shown in the box-plot in Figure 4.18. There is also an obvious increasing trend in nitrate concentrations in groundwater aquifer in forests, urban areas, and agricultural zones. DRASTIC index has been correlated with the land use categories. The dependency of DRASTIC index on land use categories and different hydrogeological parameters has been also examined (Fig. 4.19)

Instead of the appearance of high vulnerability values within the agricultural land use areas, there are still no clear correlation between the DRASTIC vulnerability index and the land use categories. As shown in Figure 4.20, agriculture practices and urban areas have a more influence on groundwater contamination with nitrate when being compared with the forest land use. Possible justification for such phenomena is the potential pollution sources from intensive fertilizers application. Urban areas have also high nitrate concentration which could be attributed to nitrate emissions from different sources like domestic-based nitrate sources and industrial activities. Nitrate concentrations under areas occupied with forests are lower than the other parts of the study area. However, nitrate is a highly mobile pollutant and move

81 fast in groundwater. This means that applied nitrate in a certain location above the land surface will eventually occur in another place within the aquifer within relatively short time, this phenomena is clearly found in the study area. It is clear that both agricultural practices and urbanization contribute to groundwater pollution with nitrate. This could be attributed to that nitrate in groundwater could not be limited to a certain practice above the ground surface as nitrate is a highly mobile solute which will move with the flowing groundwater. This fact, however, can not represented by the standard DRASTIC index in assessing the groundwater vulnerability to nitrate pollution.

From the above mentioned analysis regard to DRASTIC correlation with both land use classes and distribution of nitrate in groundwater and the corresponding results, it is clear that there is no decisive correlation between among DRASTIC, nitrate distribution in the shallow unconfined aquifer, and land use categories. Therefore, DRASTIC should be modified when being used in groundwater assessment to nitrate contamination.

82 5. ETI: APPROACH & RESULTS

5.1 Emission-Transmission-Immission (ETI) Concept

The ETI concept is a simple mass-balance model that describes pollutants transport in the subsurface system through estimating the main transport parameters. The concept is also a prognosis tool for contaminant transport with infiltrated water, and therefore provides an estimate of their inputs into groundwater table. Based on the ETI model, it is possible to assess the amounts of different pollutants that infiltrate downward to the subsurface system, move through the unsaturated zone and enter groundwater. The first parameter of the model is the emission (E) parameter which describes the amounts of a certain pollutant generated from different applications on surface of the uppermost soil layer. The second parameter, the transmission (T), describes the processes which a certain pollutant undergoes in the subsurface system. These processes include both physical transport mechanisms and biochemical transformations. The net input of a pollutant into the aquifer (the upper shallow groundwater table) is represented by the third term, the immission (I) parameter (After Azzam, 1993; Azzam and Lambarki, 2004; Lambarki, 2006).

Applying the ETI concept to describe nitrate behaviour in the subsurface system is a main focus of this chapter. The main objective will be estimating transport parameters of nitrate in different soils. After applying fertilizers or manure on the land surface, nitrate will enter the subsurface with the infiltrating water (as a solution). The rate at which nitrate enter the subsurface will be represented by the emission parameter (E). Nitrate transport in subsurface system is controlled mainly by advection and hydrodynamic dispersion. Other processes like dilution, sorption, and denitrification could also take place and retard as well as reduce nitrate leaching possibilities. Nitrate sorption and denitrification are mainly controlled by nitrate concentration (here the E-parameter) as well as the physical and the biochemical properties of soil. The above-mentioned processes that control nitrate in the subsurface system are represented together within the transmission (T) parameter. Nitrate leaching through the unsaturated zone and entering the groundwater system is described with the immission (I) parameter.

As shown in Figure 5.1, the main concept of the ETI model is based on dividing the subsurface into several sub-layers with the same thickness. Each sub-layer is then processed separately. Starting with the first uppermost sub-layer, the emission parameter (E) equals the rate of nitrate application at the land surface (as fertilizers or manure) within a given hydrological period. Afterward, the transmission parameter (T) is assessed using the new-developed laboratory setup (Advection-Diffusion Cell) (Azzam and Lambarki, 2004).

83 Through simple mass-balance calculations, the net immission of nitrate into the second sub- layer could be estimated and used as the emission parameter for further sub-layers. A series of mass-balance calculations is performed for the sub-layers matrix; the final outcome is expected to be the net nitrate input, the Immission (I), into groundwater. The Advection- Diffusion cell represents a one-layer scaled model of the ETI concept and is used to assess transport parameters of nitrate in different soils. The ETI model could be applied in many useful applications like evaluating the potential risk arising from dump sites, validating numerical models for solutes transport in the unsaturated zone. This study mainly uses the ETI model and the Advection-Diffusion cell in order adjust the DRASTIC index to estimate groundwater vulnerability to nitrate through estimating nitrate retardation in different soils and evaluating nitrate inputs to groundwater at different locations.

Fig. 5.1: The main concept of the ETI and the Advection-Diffusion cell to assess nitrate retardation.

84 5.2 Transmission Estimation Using the Advection-Diffusion Cell

Application of chemicals on the land surface is a source of groundwater pollution. Some of these chemicals will move with the infiltrating water and leach through the unsaturated layer to the groundwater table. Characterizing pollutants behaviour in the unsaturated zone is a substantial demand to assess their input into groundwater. Different methods have been used in several attempts to characterize nitrate transport in soils (see chapter 3). Some methods focused on estimating sorption parameter of nitrate in soils. Some other attempts emphasized on nitrate retardation in different soil samples. There are also some studies which highlighted nitrate reduction through the denitrification process in soils.

However, the Advection-Diffusion cell (see Figure 5.2) is a simple mass-balance tool that estimates transport parameters of a certain pollutant in soil and the amount of nitrate that will be retarded in a soil layer. The Advection-Diffusion cell has been developed and applied at the Department of Engineering Geology and Hydrogeology at RWTH Aachen University (Azzam and Lambarki, 2004). The experimental setup of the Advection-Diffusion cell has been afterward utilized to evaluate sorption parameters of some heavy metals in different soils (Hamad, 2003; Lambarki, 2006). This experimental setup will be used in this research to estimate nitrate sorption and retardation parameters for different soils. The results of the Advection-Diffusion cell could be used in numerical simulation of water and solutes in both unsaturated and saturated zone. Nevertheless, the cell will be applied in this study to validate as well as to quantitatively modify the DRASTIC vulnerability index. A major advantage of the cell is that it allows enough mixing time between the solution and the soil sample. The cell also avoids the over-mixing occurs in the batch experiments (see chapter 3 about theory and literature review for more advantages of column tests compared with the batch techniques).

5.2.1 Experimental Set-Up

The Advection-Diffusion cell is the main part of the ETI concept and is mainly used to assess solutes transport parameter including retardation in different soils. Retardation parameter could be afterward integrated in the ETI model to assess the net input of solutes leaching into the groundwater. The retardation parameter can also be used in other application like being integrated in solutes models or groundwater vulnerability indices. As shown in Figure 5.2 and Figure 5.3, the Advection-Diffusion cell allows working with soil samples with a thickness ranging from 1 to 5 cm, and a diameter of about 10 cm. However, it is recommended to have a sample thickness as small as possible to have results in a manageable time.

85

ervoir for nitrate, and [6] the collecting

Diffusion Diffusion Cell. [1] The balancing reservoirs for nitrate, [2] the balancing reservoirs for - distilled water, [3] water pump, [4] the cell with the soil sample, [5] the collecting res 2006). 2004; Lambarki, Lambarki, and Azzam (after water reservoir for Schematic Schematic diagram of the Advection

: 5.2 Fig.

86 The Advection-Diffusion cell is made up from four major parts; (1) the lower metallic plat with two connections for the exchange of distilled water, (2) a glass ring that represent the space in which the soil sample is placed after being prepared, (3) the upper and the lower cell chambers confining the soil sample, and (4) the upper metallic cover through which a certain solution flow in and out of the cell. As water-proof assurance, there should be filter stone, filter papers, and plastic O-rings between the soil sample and the cell space from the two sides (the upper and the lower sides). At the top of the whole cell body, there is a piston which holds the whole cell units together. As mentioned above, the Advection-Diffusion cell (Figure 5.3) is an integrated part of an experimental setup which is also composed of another two major parts: fluids (solution and distilled water) containers, tubes, and water pumps. This setup (Figure 5.2) will be implicitly referred as the Advection-Diffusion cell. Glass cylinders are used as fluid container either before or after the cell. A water pump is also used to keep a constant flow rate of nitrate solution and water. For more reliable results, nitrate cylinders are covered with aluminium paper to prevent the effect of light on nitrate reduction.

Fig. 5.3: The main parts of the soil cell which is the main part of the Advection-Diffusion cell used to assess transport parameters of pollutants in soils

87 5.2.2 Testing Procedure

Cell components are grouped tightly together in a way that nitrate solution (or the solution another pollutant) flows from the upper side (above the soil sample) at constant flow rate and with a constant concentration (see Fig. 5.3). The distilled water is also allowed to flow with the same flow rate through the lower chamber below the soil sample in the cell. The upper flowing nitrate flux is kept constant due to the presence of two solution containers ([1] and [3] in Figure 5.2). This cell experimental unit could be used for either diffusion or with considering the advection transport. When using the cell experimental unit for both mechanisms, a hydraulic head should be maintained through raising the first nitrate container (labelled [3] in Figure 5.2) above the distilled water one (labelled [4] in Figure 5.2) to an elevation equal to the thickness of the soil layer. The level in nitrate and water containers should be kept constant over the test period. This is achieved through having the balance containers (nitrate container and distilled water container labelled [1] and [2] in Figure 5.2). Nitrate concentration is also tested to check that it is constant over the test period (see also Fig. 5.2 and Fig. 5.3). This main reason behind keeping constant fluxes is to keep a constant concentration gradient between the upper and the lower nitrate fluxes.

The first step after preparing the soil sample is to place this prepared soil sample in the Advection-Diffusion cell as undisturbed soil sample or keeping the same field soil density. The second step is to saturate the soil sample within the cell gradually. Saturation is performed through exposing the bottom of the soil sample to the distilled water until having steady outflow of water from the upper side. The sample is saturated when volume of distilled water flowing from the upper side is constant over time. After saturating the sample, the test is started with the highest pumping rate to drain the distilled water in the upper and the lower cell chambers.

After that the pump is adjusted to the required flow rate and the cell is at this moment is ready to start with the highest concentration gradient. Starting the test means allowing nitrate flux to flow through the upper cell chamber and distilled water through the lower cell chamber with same flow rate and with a head difference equal to the thickness of the soil sample (hydraulic gradient is 1.0). Mixing process will take place between the nitrate solution entering the soil sample from the upper side (will be referred hereinafter as the input flux) and the distilled water flowing through the lower cell chamber. Nitrate flux will leave the soil sample and the cell from the lower side (will be referred hereinafter as the output flux). Solution samples are collected from the collection reservoirs (Reservoirs [5] and [6] in Figure 5.3) over the test time and are analysed to measure nitrate concentration and other parameters. Samples collection occurs at regular time intervals with daily sampling at the

88 - initial phases of the test. Sodium nitrate (NaNO 3) is used in the test as a source of nitrate. The Advection-Diffusion test has been repeated with different initial nitrate concentrations - (50, 75, and 100 mg/l as NO 3).

5.2.3 Collection of Soil Samples

Soil samples have been collected from different locations in the study area to represent the different types of soils according to their grain distribution (texture-based classification). Figure 5.4 shows a texture-based map of soil classes in the study area and the locations of the collected soil samples. There are four different classes, from which samples are collected and analysed for their physical characteristics. These classes are: sand, silty sand, sandy silt and clayey silt. Grain size distribution, water and organic matter contents, soil densities, and some other parameters have been measured for the collected soil samples. Table 5.1 summarizes the principal properties of the collected soil samples. Figure 5.5 also shows some photos representing the works included in the phase of soil sampling in the field.

5.2.4 Samples Preparation and Installation in the Advection-Diffusion Cell

The soil sample has been compacted in the Advection-Diffusion cell to have the same density as in the field. The thickness of the soil sample has been kept at 1 cm to minimize the experiment time. Other thicknesses could be also used; a soil sample with large thickness needs, however, longer time to reach equilibrium. The disturbed surface soil particles should be removed to produce a homogeneous flat surface. Dealing with small- dimensions samples allows for large negative changes due to small changes in soil homogeneity or composition. Therefore, an accurate preparation and installation of the sample is of highly importance to get an accurate representation of the field conditions. Figure 5.6 shows the main steps for preparing a soil sample in the Advection-Diffusion cell. Sample preparation starts with grouping the different parts of the Advection-Diffusion cell and collecting them together in a way to avoid any water seepage. The required amount of soil sample is then prepared and sieved through the sieve 2-mm. The soil sample is then put in the cell and compacted to have the same field density. The small soil parts are removed from the soil surface to have a smooth soil surface. Finally, the soil sample in cell is connected with the other parts of Advection-Diffusion unit and to the water pumps. The photos (1), (2), (3), and (4) in Figure 5.6 show the different parts of the cell as well as the filters and the plastic rings. The photos (5), (6), (7), and (8) in Figure 5.6 show the sequence of preparing the soil sample in the cell body before compacting it to ensure having the field density of soil.

89

Locations of the soil samples within the different soil classes in the study area the study in classes different soil the within samples the of soil Locations

: 5.4 Fig.

90

Fig. 5.5: Soil sampling in different locations in the study area

91

Fig. 5.6: Preparation of the soil sample in the Advection-Diffusion Cell

92 Table 5.1: Properties of soil samples collected and tested for the Advection-Diffusion cell (coarse-texture soils CS)

Parameter Unit CS-01 CS-02 CS-03 CS-04 CS-05 CS-06 CS-07

Sand (%) 93.44 79.99 89.72 94.35 94.35 94.17 90.32

Grain Size Distribution Silt (%) 4.36 12.31 6.35 3.51 3.27 3.38 5.15

Clay (%) 2.2 7.7 3.93 2.14 2.24 2.45 4.53

Soil Type Texture- Silty Sandy Silty Sandy Sandy SAND SAND Based SAND SILT SAND SILT SILT 93

Bulk Density (g/cm3) 1.55 1.57 1.36 1.32 1.25 1.23 1.28

Water Content (%) 2.33 4.03 5.28 4.78 6.98 7.04 8.74

Total Porosity (%) 42 41 49 50 53 54 52 Organic Matter (%) 5.27 4.47 3.07 9.83 2.73 8.52 5.77 (Loss of Ignition) o pH (CaCl2) (T=25.6 C) value 4.14 4.18 4.22 4.48 4.38 4.59 4.98

Table 5.1 (continue): Properties of soil samples collected and tested for the Advection-Diffusion cell (fine-texture soils FS)

Parameter Unit FS-00 FS-01 FS-02 FS-03 FS-04

Sand 17.38 23.09 22.42 29.64 18.97

Grain Size Distribution Silt 70.09 70.01 67.58 61.79 72.53

Clay 12.53 6.90 10.00 8.57 8.50

Clayey Clayey Clayey Clayey Clayey Soil Type Name SILT SILT SILT SILT SILT

Bulk Density 3 94 (g/cm ) 1.18 1.2 1.21 1.29 1.18

Water Content (%) 38.58 41.55 38.95 35.43 38.58

Total Porosity (%) 27 29 27 29 27 Organic Matter (%) 4.92 7.87 3.61 5.47 8.13 (Loss of Ignition)

o pH (CaCl2) (T=25.6 C) value 4.08 4.19 4.29 4.41 4.33

5.2.5 Evaluation Concept of the Advection-Diffusion Cell Results

The soil sample in the Advection-Diffusion cell is a two-phase system composed of solid and water (the soil layer and the infiltrating water). Nitrate will be transported with the infiltrating water or delayed (retarded) through different mechanisms like being attached (sorbed) to the soil minerals and the organic matter. Nitrate could be also reduced through denitrification depending on the surrounding factors. The two major transport mechanisms controlling nitrate are advection and hydrodynamic dispersion. Through advection, nitrate is transported with the bulk flowing water having its velocity and direction. Simultaneously, hydrodynamic dispersion involves both molecular diffusion and mechanical dispersion (see Equation 5.1). Nitrate is molecularly diffused due to the existence of concentration gradient between two different locations at the same time. The rate of flow is determined by the well-known Darcy’s law. The molecular dispersion of nitrate arises from the tortuosity of the porous paths in a granular porous media or the fissures in a fractures one, and the variation of groundwater flow velocity in these paths. Sorption refers to the process which retards or slows nitrate though being attached to soil organic matter or to the surface of grain particles. As sorption is a process that occurs between two phases, it mainly depends on nitrate concentration, temperature, pH value of the solution and nature of soil minerals. Sorption isotherm parameters are conceptually related to the partition coefficient of nitrate between the solution and the soil surface.

The Advection-Diffusion cell estimate in a simple mass-balance model the amount of nitrate retarded in a soil layer. The Advection-Diffusion cell does not describe in details the mechanisms of nitrate delay and therefore assess the amount of nitrate recovery (delayed nitrate). The main mechanism of the Advection-Diffusion cell experiment is to confine a prepared soil sample between two flow-fluxes (see Fig. 5.2 and Fig. 5.3); the input flux with constant flow rate and constant nitrate concentration, and the output flux. There is also a difference of the head between the two streams which is exactly the same as the thickness of the soil sample to have a unit hydraulic gradient. As soon as the test starts, it is a substantial part of the test to collect solution samples from both sides (collection reservoir number [5] and [6] in Figure 5.3) and to measure nitrate concentration electrical conductivity, pH, dissolved oxygen concentration and redox potential (Eh). The soil sample will eventually allow some of the upper stream to flow through it which mainly depends on the hydraulic conductivity of the soil sample. The input nitrate flux and the output nitrate flux are calculated from the measured nitrate concentrations and the amount of solution. The Advection- Diffusion cell used in this study will be performed under the steady state. This means that the initial nitrate concentration will be kept constant. After occupying all available sorption places

95 in the soil sample with nitrate, an equilibrium state is reached. At the time of equilibrium, nitrate flux entering the sample will totally leave it. This means, in other words, that no more sorption will take place under normal conditions.

Nitrate flux flowing through the soil sample will be subjected to the processes that would occur in a natural subsurface system (sorption, denitrification or leaching through the two main transport processes). Figures 5.7 and 5.8 show the curves related input and output nitrate fluxes used in the conceptual evaluation of Advection-Diffusion cell results. At the beginning of the experiment (corresponds to time = 0), nitrate flux will enter the soil sample at its highest rate assuming a free-nitrate sample. Afterwards, nitrate flux entering the soil sample will decrease with time. The input nitrate flux is a function of the difference between initial nitrate concentration (C0) and the measured one (Cms) from the upper side taking into accounts the effective cross-sectional area of the soil sample and the water volume flowing through the soil sample. The input nitrate flux (nitrate flux entering the soil sample from the upper side) (Jin) is assessed using the following formula (Equation 5.1) and corresponds to the curve ([1]–[2]) in Figure 5.7.

(C0 C- ms V*)  m in 2 Jin   [ mg / m ]d. ----- Equation 5.1 (After Lambarki, 2006) A( e   )t A( e   )t

Where: J in is nitrate flux entering the soil sample, C o is the initial concentration of nitrate,

C ms is nitrate concentration measured at the upper outlet of the cell (mg/L), V is solution

2 volume (L), Ae is the effective cross sectional area of the soil sample (m ), t is the time interval (days), and min is the amount of nitrate entered the soil sample (mg).

Looking at the output nitrate flux (nitrate flux which will leave the soil sample from the lower side), there will be no nitrate flowing out at the starting time of the test. The reason will be that the soil will try to use its maximum sorption capacity to retard nitrate. However, this will decrease with time as the sites available for sorption will also decrease. Consequently, the output nitrate flux will increase with time (corresponds to curve [4] in Figure 5.7) until reaching equilibrium. Nitrate flux which leaves the soil sample is also a function of the measured nitrate concentration from the lower side, the effective cross-sectional area of the sample and the water volume; it is calculated using the following formula (Equation 5.2). The equilibrium state is the time at which the input nitrate flux will flow out of it without being even partially sorbed. This is also known as the breakthrough. As previously stated and as shown in Figure 5.6, the initial nitrate concentration is kept constant over the whole time of the

96 experiment. An equilibrium state is expected to be achieved when the two fluxes have the same values and intersect together at the time of equilibrium (tq).

C( mw  )V mout 2 Jout   [ mg / m ]d. ----- Equation 5.2 (After Lambarki, 2006) Ae  t Ae  t

Where: J out is nitrate flux leaving the soil sample, C mw is nitrate concentration measured at the lower outlet of the cell (mg/L), and mout is the amount of nitrate left the soil sample (mg).

We will consider here three parameters which are related to nitrate transport through the prepared soil sample according to system represented by the Advection-Diffusion cell: nitrate recovery (Rrc), nitrate sorption (Spt), and nitrate retardation (Rt). Nitrate recovery will be assumed to represent the mount of nitrate being trapped within the cell on the soil sample (the soil minerals or the organic matter). Therefore, we will consider two main terms: nitrate sorption (in some places will be represented by nitrate recovery which is expressed in term of mass portion of the whole flowing nitrate flux, this assumption is true in the case of inhibiting any kind of nitrate reduction through biochemical processes) and retardation of nitrate which is a function of soil density and partition coefficient of nitrate. Nitrate retardation is always determined through sorption isotherms or through analytical as well as numerical simulation of the convective-dispersion equation. These three parameters are correlated and depend on the soil texture (clay minerals), nitrate solution, and the organic matter content.

Fig. 5.7: The principle of the breakthrough related to the Advection-Diffusion cell (After Hamad, 2003)

97 As illustrated in Figure 5.7 and Figure 5.8, the area confined between the two curves of the input and the output nitrate fluxes represent the amount of nitrate sorbed within the soil sample in the cell. The maximum delay capacity (nitrate recovery) will occur at the time of equilibrium and no more sorption or retardation is expected after reaching this time. The maximum recovery of nitrate is calculated through estimating the area between the two curves over the time period of the experiment (Eq. 5.3). Nitrate recovery values have been calculated as a percent (or in term of mg/kg) for the soil samples using the statistical software XACT, (SciLab, 2009). The samples reach their maximum sorption capacity when the ratio between the output flux (Jout) and the input flux (Jin) is at its maximum value (approximately one), at this time the maximum amount of nitrate will be trapped in the soil sample within the Advection-Diffusion cell.

Nitrate Delay = Rrc = (J in J- out ) dt ------Equation 5.3 (After Lambarki, 2006)

Fig. 5.8: Evaluation concept of the Advection-Diffusion Cell (After Lambarki, 2006)

98 5.3 Results of the Advection-Diffusion Cells

As discussed in previous sections, different soil samples have been collected from different locations (and from the four soil types) in the study area. The Advection-Diffusion cell tests have been performed under quasi-steady state conditions. This means that the initial nitrate concentration and the input nitrate flux are kept constant over the test period (the experiment time) which allows estimating the maximum nitrate recovery, sorption and therefore retardation parameter. The long time that the test will take is on the other hand a disadvantage of the Advection-Diffusion cell (Lambarki, 2006). The input and the output nitrate fluxes, the fluxes entering and leaving the soil sample correspondingly, are continuously measured and plotted against the time (in days). The two curves representing the two fluxes (the input and the output nitrate fluxes) will eventually meet together at the equilibrium time where the influent and the effluent of nitrate fluxes are almost equal. The time where the two curves coincide is the equilibrium time where no more retardation or sorption could occur in the case of non-reactive solutes. Retardation parameter will be directly estimated from nitrate recovery values (isotherm) and from the analytical solution of the convective-dispersive equation. This assessment assumes the nitrate sorption is equal to nitrate recovery. The main concept behind the assessment of the retardation factor (Rt) requires repeating the experiment of the Advection-Diffusion cell for the same soil sample with different initial nitrate concentrations. For each Advection-Diffusion cell, a single value will be obtained for nitrate sorption. Sorption isotherm is then obtained for each soil sample from which retardation parameter and other transport parameters could be estimated.

5.3.1 Chemical Parameters

During the Advection-Diffusion test, the specific electrical conductivity (EC), pH (H2O) value, redox potential and dissolved oxygen concentration for the collected solution samples where measured directly at time of collection and before testing the samples for nitrate concentration. The main objective of measuring both electrical conductivity (EC) and pH is to have an impression about the total dissolved solids in the solution over the test period. Organic matter content, redox potential, dissolved oxygen concentration and the pH value are also a preliminary indication of possible reduction processes.

5.3.2 Mass-Balance and Nitrate Retardation for the Coarse-Texture Soils

Firstly, the Advection-Diffusion cell has been performed for the coarse-texture soil samples (sand, silty sand and sandy silt) and with three different initial concentrations (50, 75, and 100 mg/L). These values have been selected because they represent the average nitrate

99 values of the used fertilizers for different crop and irrigation systems. The sorbed nitrate has been estimated for each soil sample at each initial nitrate concentration. As shown in Figure 5.9, there are two main phases for each coarse-texture sample. The first phase lasts for about 10 to 15 days and represents the expected results according to the theoretical concept of the cell setup. The second phase (Phase II) starts about 10 to 15 days later in which nitrate flux starts to decrease. This phenomenon could be attributed to the formation of a microbial film which will consume nitrate as a growth media and causes some biological reduction through the formation of a thin organic layer above the soil sample. This layer together with the organic matter available in the soil sample is able reduce nitrate in the trend found in these results. Within the frame of this research, the first phase will be utilized to assess the sorbed nitrate and the retardation parameter.

Fig. 5.9: The typical trend of the Advection-Diffusion results for the coarse-texture soils with initial nitrate concentration of 75 mg/L.

Both nitrate fluxes (the input and the output fluxes) have been estimated from the measured concentrations using Equation 5.1 and Equation 5.2. The plotted curves have been fitted to estimate the area under each curve as well as the confined area between the two curves. The area confined between the two curves could be also considered as the amount of nitrate sorbed within the soil sample and therefore is a representative indicator to the amount of nitrate retarded in the soil samples and will be also referred as nitrate recovery.

100 The results of the coarse-texture soil samples have a similar trend among each other. At the beginning of the test and because of the high gradient of nitrate concentration (see Fig. 5.8 and Fig. 5.9), the soil sample will be at its maximum sorption capacity. The soil sample will exhibit, at this time, the maximum sorption of nitrate. The output nitrate flux will increase in a high rate. After a relatively short time (extending from 10 days to 14 days), the output flux (nitrate flux leaving the soil sample from the lower side (water-side)) will be the same as the input flux (nitrate flux entering the sample from the upper side (solute-side)). Figure 5.10 also indicates that there is a difference between nitrate sorption (and consequently retardation parameters) among the different coarse-texture soil samples. This could be attributed, as previously mentioned, to the fact that these soil samples have different clay ratios (and probably different types of clay minerals) and different organic matter contents. Table 5.2 shows the sorption values (recovery values) of nitrate in different soil samples for the three different initial nitrate concentrations (the recovery factors or portion will be considered as the sorbed nitrate amount). It has been found that the sandy soil samples had the lowest value of the Advection-Diffusion sorption which was around 10% (expressed as a percent or a part of the total applied nitrate). The silty sand samples have relatively higher recovery factor around 15%. The sandy silt samples show the highest recovery factor with a retardation factor around 30%. This finding agrees with the literature review which stated that the more sandy is the soil, the less the retardation factor it will has (Black and Waring, 1976; Gaines and Gaines, 1994; Pathan et al., 2002; Cahn et al., 1992; Köhler et al., 2006; Sansoulet et al., 2007; Maedo et al., 2008).

Assessment of retardation factor (Rt) requires repeating the experiment of the Advection- Diffusion cell for different initial nitrate concentrations. The results of the Advection-Diffusion cell for each soil sample and at three different initial nitrate concentrations have been used to produce the sorption isotherms. A sorption isotherm is determined by comparing the sorbed concentration of contaminants to the concentration in solution. More specifically, it describes the ability of dissolved pollutants to adsorb themselves onto the solid particles (soil or organic). Sorption isotherms are often used as empirical models, which do not make statements about the underlying mechanisms and measured variables. Sorption isotherm is obtained for each soil sample from which retardation parameter and other transport parameters could be also estimated. For each soil sample, the three sorption values (could be also expressed in mg/kg) have been plotted against the initial nitrate concentrations. The three pairs of points were fitted to the three different known sorption isotherms: Linear isotherm, Freundlich isotherm, and Langmuir isotherm with their corresponding equations (see Equations 5.4, 5.7, and 5.8 respectively). Isotherms parameters have been also generated and displayed in table 5.2.

101 CS-02 CS-01

CS-06 CS-04

CS-07 CS-03

CS-05

Fig. 5.10: Nitrate fluxes (the input and the output fluxes with initial nitrate concentration of 75 mg/L) for the Advection-Diffusion cell of course-texture soil samples and

102 If the plot of the amount of a solute sorbed (S, mg/kg) versus the equilibrium concentration of the solute (C, mg/L) yields a straight line, the isotherm is call linear sorption isotherm. It is the simplest mathematical relationship (direct and linear) between the sorbed nitrate and the nitrate in the solution. Linear sorption isotherm can be described by the Equation 5.4.

Sorbed Nitrate = Rrcor S orption = Kd * C ------Equation 5.4

3 The Kd is known as the distribution coefficient [L /M] and is equal to the slope of the linear sorption isotherm. The retardation parameter could be estimated from the distribution coefficient and the particle density of the soil (see Equation 5.5).

Bd R  1  K( * ) ------Equation 5.5 t d 

Where Bd and are the bulk density and volumetric water content (or porosity in case of saturation) of the soil samples with the dimensions [M/L3] and [L3/L3] respectively. Afterwards, the advection-dispersion equation (see Equation 5.6) is solved relatively easily to assess the retardation parameter.

C 2C  C B C*  D *  v  d ------Equation 5.6 t L x 2 x x  t

Where: C is the concentration of nitrate in the liquid phase, t is the time [T], DL is the 2 * longitudinal dispersion coefficient [L /T], vx is the average linear water velocity [L/T], and C is amount of solute sorbed per unit weight of solid [M/M]. After considering the Linear sorption isotherm, the advection-dispersion equation could be written as:

C  2C  C B  K( )C  D *  v  d d ------Equation 5.7 t L x 2 x x  t

A more general equilibrium isotherm is the Freundlich sorption isotherm (Equation 5.7). Langmuir sorption isotherm (Equation 5.8) was developed to avoid that there is no upper limit to the amount of a solute (nitrate fro example) that could be sorbed.

N Sorbed Nitrate = Rrcor S orption = *K C ------Equation 5.8

1 C Sorbed Nitrate = R or S = C * ( * ) ------Equation 5.9 rc orption . 

103 Where K and N are dimensionless constants, is the maximum amount of solute that can be sorbed by the solid (M/M), and is a sorption constant related to the binding energy (L3/M).

Sorption isotherms were generated for the coarse-textured soil samples depending on the three recovery values obtained from the Advection-Diffusion cell at three different initial nitrate concentrations. The second step after producing the sorption isotherms was estimating the sorption isotherm parameters. Table 5.2 summarize the sorption isotherms’ parameters for the sic soil sample at the three different sorption isotherms.

Table 5.2: (Linear, Freundlich, Langmuir) sorption isotherms’ parameters of the coarse- textured soil sample

Langmuir Sorption Linear Freundlich Sorption 1 C Sample N =C * ( * ) = Kd C* = C*K ID S.K max Smax

Kd K N K Smax

CS-03 0.3957 1.704741 0.6878 0.0067 102.06

CS-05 0.8129 5.958122 0.5860 0.0105 173.98

CS-02 1.2998 9.11514 0.5870 0.0104 269.69

CS-06 1.7360 11.93639 0.6000 0.0098 384.47

CS-07 1.5777 9.670173 0.6228 0.0089 363.10

CS-01 0.7444 16.10525 0.3639 0.0277 117.52

CS-04 0.6883 6.964506 0.5124 0.0144 125.64

The analytical solution of the advection-dispersion equation in the case of Langmuir and Freundlich isotherms is a complex approach because of the high nonlinearity of the equation. Therefore, assessing the retardation parameter for each soil sample for its corresponding sorption isotherm is not s simple procedure using the analytical solution of the advection- dispersion equation. The solution is nonlinear and the equation is not easy to be solved. Consequently, it will be assumed that the change of sorption with nitrate concentration is constant and is equal to the unity. This assumption will simplify the analytical solution of the advective-dispersive equation. Section 5.3.3 will explain in more details solving the equation for the system of the Advection-Diffusion cell with its initial and boundary conditions.

104 5.3.2 Mass-Balance and Nitrate Retardation for the Fine-Texture Soils

The fine-texture soil samples (mainly clayey silt) have a silt portion of more than 60%. About eight samples have been tested in the Advection-Diffusion cell to estimate nitrate sorption and retardation parameter. The tests started under the same initial conditions as in the coarse-texture soils and the same laboratory environment. However, the results were totally different form those of the coarse sample. The results of the Advection-Diffusion cell in the case of fine-texture soil sample could be divided into three different phases. The experiment last for more than 120 days with no significant nitrate flux leaving the soil sample. This phenomena is attributed to the formation of a thin layer of microbial population which could have reduced the nitrate and inhibit its breakthrough. This phenomenon is proved through some photos of the cell and the soil sample after finishing the experiment and removing the part of the Advection-Diffusion cell (Figure 5.12). However, small nitrate concentrations have been measured in the outflow from the lower side of the Advection-Diffusion cells (output nitrate flux) (see Fig. 5.13). The measured nitrate concentrations and the calculated nitrate flux were not sufficient to assess sorption capacity and the retardation parameter of the fine- texture soil samples. Therefore, analytical solution of the mathematical equation controlling nitrate transport in the cell has been solved. The advective-dispersion equation has been solved for the Advection-Diffusion cell and the transport parameters have been estimated.

Fig. 5.11: A result example of one of the fine-texture soil samples (with 75 mg/L as initial nitrate concentration)

105

Fig. 5.12: Thin organic layers on the top surface of the fine-texture soil samples.

106 5.3.3 Analytical Solution and the Advection-Diffusion Cell

Solute transport in porous media is typically modeled using the generalized Advection- Dispersion equation (ADE) (Bear, 1970). The solution of ADE’s in requires discretised numerical methods, excepting the limited cases where analytical solutions exist (van Genuchten and Alves, 1982). Analytical solutions have an important role to play because they offer fundamental insight into governing physical processes, provide useful tools for validating numerical approaches, and are sometimes more computationally efficient. It was important to use a numerical simulation or an analytical solution to validate the results of the Advection-Diffusion cell in the case of coarse-texture soil samples. Moreover, the small amounts of the output nitrate flux in the case of the fine-texture soil samples make it urgent to use the analytical solution of the advective-dispersive equation. Therefore, the advective- dispersive solute transport equation (Equation 5.7) has been analytically solved for both coarse and fine soil samples. The JMP has been used for solving the convective-dispersive solute transport equation (SAS Institute Inc., 2009).

CCCCC      2 Rl   j   qC  D(  , v )l  R l =  v l  D l s l sh  sh 2 t  x  x   t  x x ------Equation 5.10

Where R is the retardation coefficient, C is solute concentration [M/L3],  is the volumetric water content [L3/L3], t is time [T], x is distance [L], q is the volumetric flux [L/T], D is the dispersion coefficient [L2/T], and v is the flow velocity [L/T]. As we have a continuous application of nitrate with constant initial concentration on the top of the soil sample in the Advection-Diffusion cell, a Dirichlet boundary condition (fixed value) has been maintained on the top of soil sample in the Advection-Diffusion cell. At time equal infinity, we will have no sorption and no retardation.

dC C (0, =t) C at =z 0, and h(0,t) = h (t), l (inf,t) = 0 ------Equation 5.11 l o o dx

Where Cl is the solution concentration of the nitrate, C0 is the initial nitrate concentration which has been maintained constant during the whole experiment. The solution is (USDA, 1982):

1  Rx  vt  2tv  Rx  vt2  1  vx 2tv   vx   Rx  vt  C / C  erfc    exp   1    exp  erfc  Equation 5.12 0           2  2 DRt  RD  4DRt  2  D RD   D   2 DRt 

107 For the fine soil samples, the analytical solution (considering the initial and the boundary conditions) was also used to derive the breakthrough curves for the different soil sample. The final results of the fine-texture soil samples are shown in Figure 5.14. High agreement between the analytical solution and the results of the Advection-Diffusion cell has been found. Figures 5.14 shows the results of the analytical solution of the advective-dispersive solute transport equation for the case of the Advection-Diffusion cell in both coarse-texture samples. Table 5.3 and 5.4 summarize nitrate recovery values and retardation capacity for all soil sample used in this study. Figure 5.13 and Figure 5.14 show the results of the analytical solution of the advective-dispersive transport equation for the Advection-Diffusion cell. The analytical solution shows a very good fitting to the results of the Advection-Diffusion cell where the average root mean square error (RMSE) for the coarse soil samples did not exceed 0.061 with a maximum value of 0.105. The average RMSE for the fine soil samples was 0.00568.

Table 5.3: Transport parameters of nitrate in the tested soil samples (coarse-texture) including retardation parameters according to the analytical solution

Nitrate Dispersion Retardation Pore Water Sample Recovery Coefficient (R ) Velocity (m/s) (%)* t (m2/s) CS - 01 16.38 1.18 4.28 e-03 3.57 e-06 CS - 02 16.01 1.29 5.10 e-03 6.92 e-06 CS - 03 10.55 1.07 4.03 e-03 1.23 e-05 CS - 04 32.00 1.17 4.51 e-03 3.47 e-05 CS - 05 9.31 1.13 3.70 e-03 2.88 e-06 CS - 06 28.00 1.15 3.53 e-03 5.23 e-06

Table 5.4: Transport parameters of nitrate in the tested soil samples (fine-texture) including retardation parameters according to the analytical solution

Dispersion Retardation Pore Water Sample Coefficient (R ) Velocity (m/s) t (m2/s) FS - 00 2.82 2.34 e-05 1.09 e-07

FS - 01 4.57 1.82 e-06 1.35 e-07

FS - 02 2.96 8.20 e-05 1.41 e-07

FS - 03 3.02 1.43 e-04 1.32 e-07

FS - 04 4.64 1.85 e-04 1.37 e-07

108

Fig. 5.13: The results of the analytical solution of the advective-dispersion equation for the Advection-Diffusion cell (coarse-texture soil samples)

109

Fig. 5.14: The results of the analytical solution of the advective-dispersion equation for the Advection-Diffusion cell (fine-texture soil samples)

110 The results of the Advection-Diffusion cell as well as the analytical solution of the advective- dispersive equation show different findings related to coarse- and fine-textured soil samples. The recovery factor of nitrate (generated by the Advection-Diffusion cell) in clearly correlated to the organic matter content in soil samples. However, this recovery factor is a general estimation of sorption capacity and retardation parameter. Nitrate recovery does not distinguish transport mechanisms. Figure 5.9 and 5.10 show that nitrate advection will occur at the early stages of the test where dispersion will start lately. A precise insight in Figure 5.10 illustrate that nitrate is advected in the sandy soils samples relatively faster than in the other samples. The dispersion of nitrate in silty sand and sandy silt samples seems to be more dominant than the advection process which will delay nitrate transport. Generally, nitrate recovery is a very good and useful indicator for nitrate sorption and retardation in soils. The retardation parameter is very good correlated to both clay content and organic matter content in the soil samples. High values of retardation parameter occur at high level of organic matter content in the soil samples. Retardation parameter is also correlated to the nitrate recovery values of the Advection-Diffusion cell.

111 6. DRASTIC & ETI COUPLING

6.1 Introduction

As it has already been discussed in Chapter 4, the standard DRASTIC index is an intrinsic method and is subjected to a high degree of subjectivity. These features have been supported through comparing the final groundwater vulnerability map with the actual nitrate concentration in the shallow unconfined groundwater aquifer. A thorough assessment of the DRASTIC results with a descriptive insight into its relation to the actual nitrate concentrations in groundwater will support our finding which stated that we acquire an adjustment of this index. The next step after assessing groundwater vulnerability and the transport parameters of nitrate in different soils in the study area is to couple the ETI concept (nitrate retardation) with the standard DRASTIC index. Combining the retardation parameter of nitrate in different soils in the standard DRASTIC index will avoid the subjectivity if the index and make it more specific to be used in the case of nitrate in groundwater. This adjustment has been considered to be an effective tool as it considers nitrate retardation in soil which is one of the most important parameters and processes controlling nitrate movement if the subsurface system. As retardation is a soil-related parameter, coupling between DRASTIC and the ETI concept could be achieved through modifying either soil parameter, or vadose zone.

6.2 Conceptual Background

Chapter four discussed the assessment of the intrinsic groundwater vulnerability in the study area (Venlo Block). DRASTIC index is composed of seven hydrogeological parameters. The uppermost soil layer and the impact of the vadose zone are two important parameters in the context of nitrate transport involved in the DRASTIC index. Retardation of nitrate (due to sorption or denitrification) could take place either in the uppermost soil layer or in the deeper vadose zone. Retardation of pollutants will increase the capacity of the covering layers in protecting the underlying groundwater aquifer. However, DRASTIC modification and adjustment will be achieved in this study through modifying the soil parameter (S) using the capacity of soil to sorb as well as to retard nitrate. It is expected that integrating the retardation parameter within the standard DRASTIC index will reduce the vulnerability index. The Advection-Diffusion cell experiment was applied for different soil samples (different types) from different locations in the study area. Chapter five discussed the main concept of the cell and its results from which three parameters were obtained: nitrate recovery factor

(Rrc) which was directly calculated form the results of the Advection-Diffusion cell. The recovery parameter was assumed to represent the amount of nitrate sorbed within soil sample. Therefore, sorption isotherm was plotted for each soil sample from which transport

112 parameters were afterwards generated. Moreover, the convective-dispersive solute transport equation was analytically solved and nitrate transport parameters (including nitrate retardation Rt) were estimated for the different soil types. The standard DRASTIC index was modified through considering nitrate recovery factor (sorption) and the estimated retardation parameters. The following sub sections explain the methodology of DRASTIC modification.

6.2.1 Modifying the Soil Parameter (S) with the Recovery Factor (Rrc)

The recovery factor (Rrc) has been assigned to each class of the uppermost soil layer in the study area. This step enabled obtaining a new surface map (a raster image) with the recovery factors of nitrate in different soils that have been assessed using the Advection- Diffusion cell. A new modified soil parameter (S’) was obtained due to multiplying the original soil parameter (according to the standard DRASTIC index) with the assessed nitrate recovery factor (Rrc). The soil parameter in the standard DRASTIC index was replaced with a new one. The new DRASTIC formula will be written as (Equation 6.1):

DRASTIC Vulnerability Index ' ------Equation 6.1 DVI (a) = D Drw + R Rrw + A w Ar + (S rw +)S T Trw + I I rw + C Crw

' (SwSr ) is the modified parameter corresponding to the top soil and is calculated considering the recovery factor using the formula S'= S× -(1 R rc ) , Rrc is the delay of nitrate and is expressed here in percent

6.2.2 Subtracting the Recovery Factor (Rrc) as New and Single Parameter

Secondly, the DRASTIC index was modified through subtracting the assessed recovery factor as a single parameter (using the ETI and the Advection-Diffusion cell experiment) from the standard DRASTIC index using the formula in (Equation 6.2).

DRASTIC Vulnerability Index ------Equation 6.2 DVI (b) = D Drw + R Rrw + A w A r + S Srw + T Trw + I I rw + C w Cr - R rc

6.2.3 Subtracting the Retardation Parameter (Rt)

As discussed in Chapter 5, the parameters of nitrate retardation in soil were estimated using two different approaches. Firstly, it was assumed that the recovery factor Rrc (was considered as the same as the sorbed nitrate and was assessed using the Advection-Diffusion cell) represents nitrate sorption in soil. These values were used to produce sorption isotherms in

113 soil from which retardation parameter was also estimated. Secondly, the convective- dispersive solute transport equation was analytically solved to predict retardation parameter for the different soil samples considering the experimental results obtained from the Advection-Diffusion cell. The average values of the two retardation parameters was considered and used as a third approach to modify the standard DRASTIC index through the soil parameter. The soil parameter was divided by the corresponding retardation parameter

(Rt) and then summed up to the other six parameters of the standard DRASTIC index (Equation 6.3). This concept aims to minimize the vulnerability index using the capacity of soils to retard nitrate.

DRASTIC Vulnerability Index

S Srw ------Equation 6.3 DVI (c) = D Drw + R Rrw + A A rw + ( +) T Trw + I I rw + C Crw R t

6.3 Adjusted DRASTIC Vulnerability Indices

The standard DRASTIC index was modified through the generated transport parameters of nitrate in soil. Recovery factor and retardation parameter were used in three different approaches to modify the DRASTIC index. The groundwater vulnerability (considering the factor of nitrate recovery in soil as well as nitrate retardation parameter) has been evaluated using three modified DRASTIC indices. The results from the three modified DRASTIC indices have been compared with the results of the standard DRASTIC model (the intrinsic index) as well as with the actual nitrate concentration in the upper unconfined groundwater aquifer in the study area in the Venlo Block. The results of this research underscore the importance of conducting vulnerability analysis in the Venlo Block region and enacting contaminant control measures for those contaminants that demonstrate mobility and persistence in the agricultural and urban environment, such as nitrate.

A new groundwater vulnerability assessment according to a nitrate-specific version of the widely used DRASTIC model is proposed in this study. Using the original DRASTIC method, the vulnerability presented high values, mainly due to the texture of the sediments and the low depths of waters, which increase the accessibility of the contaminants to groundwater. The new vulnerability indices consider the capacity of different soils to sorb as well as to retard nitrate. After modifying the standard DRASTIC index using the retardation parameter of soils, the new DRASTIC indices maps were generated and compared with the standard map as well as with nitrate concentrations in groundwater a long the flow directions which are perpendicular to the piezometric head illustrated in Figures 6.4-6.6. DRASTIC with sorption capacity (or retardation parameter) of soils showed specifics results for nitrate.

114 Ratings and weightings values were kept constant in the two modified DRASTIC indices as the same value given by Aller et al., (1987) for the soil parameter. In this study, sorption capacity of soils of nitrate was estimated using the Advection-Diffusion cell and was afterward integrated in the standard DRASTIC index. The soils in the study area have variable sorption capacity for nitrate. Considering this variation and the influence of sorption and retardation on nitrate transport, the new parameters were assigned the same value of weight and rates of the soil parameter in the standard DRASTIC index. This means that the S’ parameter, the new added parameter of recovery factor (nitrate sorption) of nitrate in soil

(Rrc), and nitrate retardation (Rt) were assigned the same values of ratings and weights as those for the standard soil parameter (S). This is because of two main reasons: firstly, these values are literature values; secondly, a comparison between the standard and the modified DRASTIC index needs keeping these values constant to reflect the effect of the recovery factor and hence to consider both sorption and retardation parameters.

To modify the DRASTIC groundwater vulnerability index within the ArcGIS, a number of individual raster files, or map layers, were constructed: topography, soils, depth to water, impact of vadose zone media, aquifer media, net recharge, hydraulic conductivity, sorption capacity of soils, and nitrate retardation. The resulting standard DRASTIC index was between 89 and 233. This range was divided into four classes: (a) 89-100 (low vulnerability), (b) 101-150 (moderate vulnerability), (c) 151-200 (high vulnerability), and (d) 201-233 (extreme or very high vulnerability). As It has been previously mentioned, only about 8% of the area is classified as low vulnerable, 10% of the area is classified to have moderately vulnerable groundwater system and the rest of the area (more than 85%) is highly to extremely vulnerable to pollution.

Using Equation 6.1, Equation 6.2, and Equation 6.3, three new vulnerability indices were calculated. As seen in Figure 6.1, the first modified vulnerability index, DVI (a), has reduced the vulnerability of the study area where three classes were found: (a) 67-100 (low vulnerability, 101-150 (moderately vulnerable), and 151-199 (high vulnerability). The second modified index (DVI (b)) is shown in Figure 6.2 and displays the range between 43 and 224. This index has been also divided into five vulnerability classes with slight decrease in vulnerability value compared with the standard DRASTIC index. The third modified DRASTIC index shown in Figure 6.3 also showed a decrease in the standard vulnerability index with a value ranging from 67 to 201 and has four vulnerability classes: 67-100 (low vulnerability), 101-150 (moderately vulnerable), 151-200 (high vulnerability), and 201-214 (extremely vulnerable).

115

after after modifying the Soil parameter using nitrate

TIC) ’

Groundwater vulnerability map (DRAS sorption or recovery factor or recovery sorption

:

6.1 Fig.

116

) rc

ETI) after subtracting nitrate sorption (recovery R from the standard DRASTIC index DRASTIC the standard from -

Diffusion Cell -

ter ter vulnerability map (DRASTIC

Groundwa assessed using the Advection using assessed

:

6.2 Fig.

117

ugh dividing it

) ) after modifying the Soil parameter thro t

R

-

by the corresponding retardation parameter retardation corresponding by the Groundwater Groundwater vulnerability map (DRASTIC

:

6.3 Fig.

118 It is clearly shown in Figure 6.1, Figure 6.2, and Figure 6.3 that vulnerability values were decreased in the new maps. This decrease in vulnerability is attributed to considering possible retardation of nitrate in different soils in the study area which was not considered in the standard DRASTIC index. Moreover, a new class of vulnerability appears in one of the modified DRASTIC indices and labeled as “very low” groundwater vulnerability. This decrease in the groundwater vulnerability is attributed to considering the capacity of soils to sorb as well as to retard nitrate.

On the other hand, comparing the new DRASTIC indices indicated more agreement between nitrate concentrations in groundwater and the vulnerability index. This is apparently clear in the western zones of the study area where nitrate concentrations are relatively low and no agricultural practices exist in the area. Comparing the results of groundwater vulnerability assessment at the study area using the new DRASTIC indices with that of nitrate contamination investigation, the spatial correlation is quite good. In other words, places such as Heidhausen and Bracht in the western part of the study area, where low concentrations of nitrate have been detected correspond quite well with those with lower value of the new modified DRASTIC indices (DRAS’TIC and DRASTIC-Rrc, DRASTIC-Rt). Therefore,

(DRAS’TIC and DRASTIC-Rrc, DRASTIC-Rt) might be useful tools for groundwater pollution potential assessment in both agricultural and urban areas. Moreover, the results of groundwater vulnerability assessment using the (DRAS’TIC and DRASTIC-Rrc, DRASTIC-Rt) reflect the tendency of nitrate concentrations in groundwater.

119

the standard DRASTIC index, (B) is theof (B) map index, DRASTIC standard the

and Nitrate in Groundwater: (A) is the (A) of map Groundwater: in and Nitrate groundwater. in distribution nitrate of map the is (c) and index, DRASTIC modified the The standard DRASTIC vulnerability index versus the modified DRASTIC index (DRASSTIC) index the versus index The DRASTIC modified vulnerability (DRASSTIC) DRASTIC standard

:

6.4 Fig.

120

ex

: (A) is the map of the standard DRASTIC index, (B) is ater

) ) and Nitrate in Groundw

rc The The standard DRASTIC vulnerability index versus the modified DRASTIC ind _R

:

TIC 6.5

Fig. (DRAS groundwater. indistribution nitrate of the map andis (c) index, DRASTIC the theof modified map

121

ex

: (A) the is ofmap the standard (B) DRASTIC index, is the

and inNitrate Groundwater )

t The The standard DRASTIC vulnerability index versus the modified DRASTIC ind _R

:

TIC 6.6

Fig. (DRAS groundwater. in distribution nitrate of map is the (c) and index, DRASTIC ofmodified map the

122

TIC TIC indices d DRAS versus the three modifie the versus three The standard DRASTIC vulnerability index index vulnerability DRASTIC standard The

: 6.7 Fig.

123 Figure 6.7 illustrates box-plots for the relationship between nitrate concentrations in the upper unconfined aquifer with the vulnerability classes of the standard and the modified DRASTIC indices. These box-plots show that nitrate concentration is more correlated to vulnerability classes in the modified indices as in the standard index. This could be attributed to the more significance that has been assigned to the soil parameter in the DRASTIC index which represented soil capacity to sorb (or to retard) nitrate before leaching to the groundwater table. The groundwater vulnerability to pollution using the DRASTIC original method indicated high values, mainly due to the texture of the sedimentary deposits distributed over a large part of the study area, which favours the accessibility of the pollutants to the saturated zone. The contamination vulnerability maps made with the sorption parameter (recovery factor) as well as with the retardation parameter indicated significant difference compared with the vulnerability map made using the standard DRASTIC index.

This means that the standard DRASTIC index overestimates the groundwater vulnerability to nitrate pollution. This finding has been supported with the results from the statistical correlation between nitrate distribution in the upper unconfined groundwater aquifer and the vulnerability values. Nitrate distribution has been found to be strongly correlated to the vulnerability value in the new modified indices. Nitrate distribution in groundwater was correlated to the DRAS’TIC index with a correlation coefficient of 0.827. The correlation coefficient is 0.814 between nitrate distribution in groundwater and the DRASTIC-Rrc and is

0.913 between nitrate distribution in groundwater and the DRASTIC-Rt.

6.4 Sensitivity Analysis of the New DRASTIC Indices

The map removal sensitivity analysis of the soil parameter (the modified one) in the adjusted DRASTIC equations has been conducted according to the concept presented in (Almasri, 2007a). The results showed that removing the modified soil parameter, according to the nitrate retardation parameter (Rt), from the (DRASTIC-Rt) index has the highest sensitivity among the other two modified indices. The sensitivity variation index (SVI) was 27.35 due the removal of the soil parameter from the (DRASTIC-Rt) and was 19.57 due to the removal of modified soil parameter (S’), considering nitrate recovery parameters. These findings reinforce that the (DRASTIC-Rt) is the mostly suitable index to be used to assess the groundwater vulnerability to nitrate contamination in the study area.

124 7. SUMMARY & RECOMMENDATIONS

7.1 Summary

This research aimed to assess groundwater vulnerability to nitrate pollution in a part of the Venlo Block in the northwestern regions of the North Rhine-Westphalia. Assessment of groundwater vulnerability in the study area has been achieved after modifying the standard DRASTIC index using the ETI concept and the Advection-Diffusion cell. The first part of this research focused on using the intrinsic DRASTIC index to assess groundwater vulnerability. Necessary data for land use, soil, and geological and hydrogeological settings in the study area were collected from different sources. In this study, the empirical DRASTIC index model and GIS technique were employed to assess the aquifer vulnerability of the shallow aquifer of the study area. Seven environmental parameters including “Depth to water”, “net Recharge rates”, “Aquifer media”, “Soil media”, “Topography”, “Impact of vadose zone”, and “hydraulic Conductivity” were used to represent the natural hydrogeological setting. These data have been used to build the seven parameters of the DRASTIC vulnerability index. The final groundwater vulnerability map was constructed. It was clear that more than 90% of the study is classified to have “high” to “very high” groundwater vulnerability. Moreover, according to the results of the groundwater vulnerability assessment, the study area can be divided into four zones: low groundwater vulnerability risk zone (risk index 89-100); moderate groundwater vulnerability risk zone (risk indexes 101-150), high risk zone (risk index 151- 200) and very high risk zone (risk index 201-233). Under the natural conditions, the “high” and the “very high” groundwater vulnerability risk zones of the area are mainly located in the groundwater recharge zones, shallow groundwater and around the surface water bodies. The “low” groundwater vulnerability risk zones cover around 10% of the area which is characterized by silty soil and high land slope.

Correlation between DRASTIC results and nitrate concentration in groundwater was investigated to test the effectiveness of using DRASTIC in vulnerability assessment. Regression analysis was performed to check the DRASTIC map and to see to what extent the map agrees with the actual nitrate pollution situation. In this context, single regressions and box-plots between DRASTIC results and groundwater contamination were done. Geostatistical analysis of water samples showed that nitrate concentration in the groundwater in the middle and the lower parts of the study area is more than nitrate concentration at north and northeast parts. These results could be attributed to the intensive agricultural practices and urbanization in those zones as well as to the direction of groundwater flow. Correlation results between nitrate concentration map in the groundwater shows that nitrate concentration has - relatively - the best correlation to both hydraulic

125 conductivity and aquifer media (coefficient of correlation r = 0.484) followed by depth to water table parameter (r = 0.413) which is also coincide with the correlation results between DRASTIC and its parameters. A weak correlation was found between nitrate concentrations and the impact of the vadose zone and the recharge rate (r = 0.065 and 0.121 respectively). It was also found that nitrate concentrations were relatively poorly correlated to the slope of the land surface (r = 0.238). This shows that most effective parameters on groundwater pollution with nitrate in the study area are the depth to water table and hydraulic conductivity respectively.

The map removal sensitivity analysis indicated that vulnerability index seems to be most sensitive to the impact of vadose zone and slope of the land surface. In addition, soil texture and recharge rate seem to pose a high influence on the vulnerability index. The effective weight is a function of the value of the single parameter with regard to the other six parameters as well as the weight assigned to it by the DRASTIC model. Soil texture, depth to groundwater table and the impact of vadose zone (the protective layer) tend to be the most effective parameters in the vulnerability assessment (mean effective weight is 21.5%) while the hydraulic conductivity and aquifer media are considered to have lower significance in term of effective weights. Additionally and as discussed previously, these four parameters have relatively large variation over the entire study area compared with the aquifer media and hydraulic conductivity and therefore, these four parameters are more sensitive and have significant impact on the change of the final assessed DRASTIC index.

Comparisons between factors affecting vulnerability and nitrate concentrations in groundwater allowed pointing out correlations around the value 0.5 among these parameters. In fact these parameters describe the phenomenon trend and highlight the maximum nitrate concentrations, which can be found in particular circumstances. Nevertheless the correlation graphics often show wide ranges of possible nitrate concentrations related to the cases which were studied, and do not allow to assess univocal concentration values. The correlation between nitrate concentrations and single factors suggests that there might be other elements affecting nitrate concentrations in groundwater. The same situation could be observed by comparing vulnerability index and nitrate concentrations in groundwater. This shows that some important parameters are not well represented by DRASTIC, especially after proving that DRASTIC index is highly sensitive to these parameters (as discussed in the section of sensitivity analysis). On the other hand, these parameters are very important in controlling solutes transport and therefore, should be properly considered. Soil, for example, control nitrate transport and transformation processes and its contribution in groundwater protection should be properly reflected in DRASTIC vulnerability assessment.

126 This was the main reason for considering nitrate retardation in soil as a modifying factor or as a new factor in the DRASTIC vulnerability index. The Advection-Diffusion cell was used to assess this parameter and it was found that it is mainly function of the soil texture. The Advection-Diffusion cell has been applied for different soil samples collected from different places and soil classes in the study area. The Advection-Diffusion cell has been repeated for three different initial nitrate concentrations to enable extracting the sorption isotherm for the different soil samples. The amount of nitrate being sorbed within the soil sample has been directly estimated using the Advection-Diffusion cell. It has been found that this parameter depends on the texture of the soil (the grain size) and on the amount of organic matter in the soil sample. Nitrate retardation has also been assessed using the sorption isotherm achieved from repeating the Advection-Diffusion cell for different initial nitrate concentrations. Nitrate retardation was also dependant on the organic matter content as well as on the grain size distribution of the soil samples.

Coupling nitrate retardation parameter with the standard DRASTIC index has decreased vulnerability values within the study area. A new vulnerability class “very low” appeared in the study area which reflect the low groundwater vulnerability in the forests land use class and coincide with the low nitrate concentration in groundwater within the same zone. It was clear that the new modified DRASTIC indices are more correlated to both nitrate concentration and land use classes. This means that the standard DRASTIC index overestimates the groundwater vulnerability to nitrate. In other words, using the Advection-Diffusion cell to adjust the DRASRIC index produce more accurate groundwater vulnerability index. This new modified DRASTIC index - based on the ETI concept - is more objective and less intrinsic and could produce a quantitative map for groundwater vulnerability.

8.2 Recommendations

The results of the Advection-Diffusion cell should be also calibrated with other laboratory techniques and fields measurements. This will enable further research to assess the feasibility of different alternatives to remove nitrate from soil and groundwater. It is also advised to apply the Advection-Diffusion cell together with the ETI concept to assess retardation of other pollutants, especially pesticides, in different soils. Applying the Advection-Diffusion cell for stable pollutants (tracers) will eventually serve as a reference for calibrating the Advection-Diffusion cell for further applications.

127 It is recommended to apply other vulnerability indices in the study area and compare them with the DRASTIC vulnerability index. Further field research and laboratory works are still needed to be conducted to help quantify the influence of factors such as soil variability, vadose zone lithology, and aquifer chemistry on groundwater nitrate pollution. More accurate data related to fertilizers types and their rates of applications is desirable to be integrated with both DRASTIC index and the ETI concept.

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146

Curriculum Vitae

Name Taiseer Aljazzar

Birth 26.10.1978 Khanyounis, Gaza Strip, Palestinian Territories

2006 – 2009 Research assistant at the Department of Engineering Geology and Hydrogeology, RWTH Aachen University, Aachen, Germany

2005 German language courses at Goethe Institute, Bremen, Germany

2003 – 2005 Project coordinator at Palestinian Environmental Friends Association, Rafah, Gaza Strip, Palestinian Territories

2001 – 2003 Master’s degree in Civil Engineering (Water Resources and Environmental Engineering), Jordan University of Science and Technology, Irbid, Jordan

2000 – 2001 Research assistant at the Environmental and Rural Research Center (ERRC), Islamic University of Gaza, Gaza Strip, Palestinian Territories

1995 - 2000 Bachelor degree Civil Engineering – Islamic University of Gaza, Gaza Strip, Palestinian Territories

1995 Secondary school certificate at Rafah Secondary School, Rafah, Gaza Strip, Palestinian Territories