Preface

Interest in evaluating the impact of human activities on the environment has increased within the last decade. This interest is reflected in issuing stricter regulations and resources allocations to prevent and control potential negative impacts associated with different human activities. Environmental pollution is one of the adverse impacts of human activities; it is associated with historical and improper routine and accidental release of pollutants into the environment. In general, kinetic models are used to evaluate the driving forces that initiate temporal changes in any system and quantify these changes. These models are widely applied to design and optimize systems that support pollution prevention and control measures, i.e. different waste management activities and remediation projects. This book aims to present advances in developing and applying different kinetic models to support pollution prevention and control efforts. The authors summarize their experiences and present advances in different fields related to the presented topics. The book targets professional people in environmental industry and readers with technical backgrounds such as graduate and postgraduate students undertaking courses in environmental chemistry, ecology, and environmental engineering.

The book consists of three sections that cover important research and development efforts in modeling environmental systems. The first section introduces the assessment models as tools to support pollution prevention and control decisions. The editor describes the integration between the research and assessment models with special emphases on pollutant migration, presents the iterative nature of the assessment models, and explains the development of conceptual models by illustrating models that could be used to predict pollutant migration in different environmental subsystems. The chapter presents computational model selection and highlights simple models that could be used to estimate migration in terrestrial subsystems.

The second section highlights the development of kinetic models that could be used to support research efforts in preventing and controlling pollution generation. Prof. Liu and Dr. Wang present the development of a model that could be used to understand and analyze the physical mechanism and non-equilibrium condensation growth kinetics of carbon particles released from diesel engines. The chapter explains the condensation growth process and its control for soot particles using the Monte Carlo method. Dr. Hamad et al. develop a kinetic model to describe polyvinyl alcohol degradation in the advanced oxidation process. The model considers photolysis, polymer chain scission, and mineralization reactions to describe degradation, and the effect of the operating conditions are evaluated. The statistical moment approach was applied to model the molar population balance of live and dead polymer chains taking into account the probabilistic chain scissions of the polymer.

The third section displays environmental assessment studies for herbicide application, and development of a conceptual model for strategic environmental assessment. Prof. Jensen et al. present the results of a research effort to identify the features of ionizable and non-ionizable herbicides on volcanic ash-derived soils. The chemical and physical properties of both variable- and constant-charge soils are introduced and the sorption of metsulfuron-methyl onto both soil types is illustrated. Several models are tested to describe the sorptive behavior of ionizable and non-ionizable herbicides. Dr. Swangjang illustrates the development of a conceptual model to support strategic environmental assessment for mega projects. Consideration of the selection of the objectives, targets, and indicators is presented. A case study is presented considering the kinetic development resulting from changes in land use and ecological impacts are investigated. Finally, the conceptual model is presented.

I would like to thank cordially all the authors for their efforts that led to the production of this distinguished scientific contribution. An especial acknowledgment is directed to the author service manager, Ms. Marina Dusevic, for her coordination efforts.

> **Rehab O. Abdel Rahman** Atomic Energy Authority of Egypt, Cairo, Egypt

> > Section 1

Introduction

1

Section 1 Introduction

Chapter 1

Introductory Chapter:

Rehab O. Abdel Rahman

1. Introduction

models are applied to:

3

treatment technologies;

Development of Assessment

Models to Support Pollution

Preventive and Control Decisions

The continuous increase in human activities affects the environment in notable ways; these effects need to be monitored and controlled when appropriate to ensure the sustainability of our lives. Environmental pollution is one of the major problems that associate these activities; it is initiated when a substance is released into the environment in a way that prevents its natural restoration [1, 2]. These releases could be classified as planned and uncontrolled releases. The first class is a part of routine human activity where discharge is performed after complying with the regulatory requirements, whereas uncontrolled releases associate accidents and nonregulated activities [1]. Uncontrolled releases and historical practices have led to several contamination problems, so restoration or remediation programs are being initiated to control these problems from spreading [2]. Currently, preventing and controlling environmental pollution and restoration of affected environmental systems receive great attention globally. This attention was translated into issuing strengthen regulations and allocating natural and human resources to support pollution prevention and control activities. In this respect, a continuous increase in research efforts was dedicated to investigate new materials and/or systems to evaluate their potential applications in preventing and controlling environmental pollution, that is, wastewater, gaseous, and solid waste management, and in and ex situ remediation projects. Table 1 lists some pollution control and prevention systems and their classifications in terms of the scientific bases of the used technologies. These investigations are supported with enormous efforts to understand, simulate, predict, and decide on the performance of these materials and systems under predefined conditions using wide range of models. In this context, kinetic

1. assess the formation and/or evolution of the system and its subsystems;

3. design and optimize the operation of remediation projects; and

systems, that is, planning, design, licensing, etc.

2. assess, control, and optimize the chemical reactions used in different waste

4.support the decision-making process at regulatory agencies and operational facilities during different life cycle phases of pollution control and prevention

## Chapter 1

## Introductory Chapter: Development of Assessment Models to Support Pollution Preventive and Control Decisions

Rehab O. Abdel Rahman

## 1. Introduction

The continuous increase in human activities affects the environment in notable ways; these effects need to be monitored and controlled when appropriate to ensure the sustainability of our lives. Environmental pollution is one of the major problems that associate these activities; it is initiated when a substance is released into the environment in a way that prevents its natural restoration [1, 2]. These releases could be classified as planned and uncontrolled releases. The first class is a part of routine human activity where discharge is performed after complying with the regulatory requirements, whereas uncontrolled releases associate accidents and nonregulated activities [1]. Uncontrolled releases and historical practices have led to several contamination problems, so restoration or remediation programs are being initiated to control these problems from spreading [2]. Currently, preventing and controlling environmental pollution and restoration of affected environmental systems receive great attention globally. This attention was translated into issuing strengthen regulations and allocating natural and human resources to support pollution prevention and control activities. In this respect, a continuous increase in research efforts was dedicated to investigate new materials and/or systems to evaluate their potential applications in preventing and controlling environmental pollution, that is, wastewater, gaseous, and solid waste management, and in and ex situ remediation projects. Table 1 lists some pollution control and prevention systems and their classifications in terms of the scientific bases of the used technologies. These investigations are supported with enormous efforts to understand, simulate, predict, and decide on the performance of these materials and systems under predefined conditions using wide range of models. In this context, kinetic models are applied to:



computational models will be presented, where some simple models that could be used to estimate the migration in terrestrial subsystems will be summarized.

Introductory Chapter: Development of Assessment Models to Support Pollution Preventive…

Assessment models are used to support the decision-making process during different life cycle stages of any pollution prevention and/or control system, for example, sitting waste management facility and designing remediation program. Their outputs should provide assurance that the systems will be sited, designed, operated, etc., in a manner that compiles with the safety requirement issued by the regulatory body. Assessment modeling starts with problem formulation and basic system description based on available system information. During problem formulation, the assessment objectives and audiences, regulatory framework, system boundaries, spatial and temporal scales, stage of project development, critical receptors (affected groups), adopted assessment approaches, nature of assumptions, data availability, level of accuracy, cost, and uncertainty treatment should be clarified [4]. The level of the assessment complexity is largely dependent on the national regulations and state of project development. Assessment modeling is an iterative process, where basic system data are used to develop a simple model that contains all essential FEPs derived based on basic system description. The model is then verified using system-specific data to check its prediction adequacy. If adequate simulation results are obtained, the model will be applied; otherwise more system-specific data should be collected to help in improving the model predictions. Figure 2 illustrates the iterative nature of the modeling process and its relation with the system-specific data, in which the developed model complexity or simplicity is determined based on the stage of development of the studied system and the availability of system-specific data [11, 12]. The developed model, in each iterative stage, is produced from multi-step process that includes the development of conceptual and computational models (mathematical model and the tool that solves the

2. Iterative nature of the assessment modeling

Integration of research and assessment models in studying a system.

DOI: http://dx.doi.org/10.5772/intechopen.83822

Figure 1.

mathematical model) [5–9].

5

#### Table 1.

Technology for preventing and control of environmental pollution

Modeling by definition is an abstract of the real systems, where essential features, event, and process (FEP) that affect the performance of the studied system are presented [3, 4]. Generally, the modeling efforts are divided into research and assessment models. Research (process) models use laboratory and field experiments to identify FEPs that affect a subsystem or more, whereas assessment models link important processes (determined from research model) to predict the overall system performance [5, 6]. Figure 1 illustrates the integration of research and assessment models, in which the studied subsystems are characterized and the factors that affect their behavior are identified experimentally. Then models are used within the research efforts to interpret, extrapolate/interpolate, and optimize the collected data; the modeling results will be used to evaluate and rank the FEPs that affect the system. In assessment models, important FEPs are linked to identify the problem formulation and basic system description, and then conceptual and computational models are constructed, verified, and used [5–11]. For instance, the quantification of the effect of time on the pollutants migration in terrestrial, aquatic, and/or atmospheric subsystems is usually conducted by measuring the concentration of major pollutants at incremental time at different distances from the source. Experiments are run for specified time determined based on the temporal scale of the study. The collected experimental data are analyzed to quantify the processes that control the migration. This analysis might include the use of simple empirical, semiempirical, or mechanistic mathematical models that allow a clear understanding of the nature of the processes that affect the migration. In terrestrial subsystems, these processes might include percolation, retardation, biodegradation, advection, and hydrodynamic dispersion [8, 11]. In subsequent sections, the development of assessment models to support the decision-making process will be illustrated with special emphasis on the prediction of pollutant migration. In this respect, the iterative nature of the assessment modeling will be overviewed, the conceptual model will be introduced, and some conceptual models that could be used to predict pollutant migration will be illustrated. The selection of

Introductory Chapter: Development of Assessment Models to Support Pollution Preventive… DOI: http://dx.doi.org/10.5772/intechopen.83822

#### Figure 1. Integration of research and assessment models in studying a system.

computational models will be presented, where some simple models that could be used to estimate the migration in terrestrial subsystems will be summarized.

## 2. Iterative nature of the assessment modeling

Assessment models are used to support the decision-making process during different life cycle stages of any pollution prevention and/or control system, for example, sitting waste management facility and designing remediation program. Their outputs should provide assurance that the systems will be sited, designed, operated, etc., in a manner that compiles with the safety requirement issued by the regulatory body. Assessment modeling starts with problem formulation and basic system description based on available system information. During problem formulation, the assessment objectives and audiences, regulatory framework, system boundaries, spatial and temporal scales, stage of project development, critical receptors (affected groups), adopted assessment approaches, nature of assumptions, data availability, level of accuracy, cost, and uncertainty treatment should be clarified [4]. The level of the assessment complexity is largely dependent on the national regulations and state of project development. Assessment modeling is an iterative process, where basic system data are used to develop a simple model that contains all essential FEPs derived based on basic system description. The model is then verified using system-specific data to check its prediction adequacy. If adequate simulation results are obtained, the model will be applied; otherwise more system-specific data should be collected to help in improving the model predictions. Figure 2 illustrates the iterative nature of the modeling process and its relation with the system-specific data, in which the developed model complexity or simplicity is determined based on the stage of development of the studied system and the availability of system-specific data [11, 12]. The developed model, in each iterative stage, is produced from multi-step process that includes the development of conceptual and computational models (mathematical model and the tool that solves the mathematical model) [5–9].

Modeling by definition is an abstract of the real systems, where essential features, event, and process (FEP) that affect the performance of the studied system are presented [3, 4]. Generally, the modeling efforts are divided into research and assessment models. Research (process) models use laboratory and field experiments to identify FEPs that affect a subsystem or more, whereas assessment models link important processes (determined from research model) to predict

Chemical Advanced oxidation - - Chemical Stabilization

Aerobic, Low/High-Anaerobic Digestion

Thermal Evaporation Incineration Combustion Incineration,

Wastewater Solid waste Gaseous waste Remediation


Cyclone, Bag-House, Electrostatic precipitator

Filters, Sorption

Segregation, Compression, Shredding

In-& ex-situ

Soil washing, Soil vapor extraction

barrier, Electro-Kinetic


Permeable reactive

Ex-situ-slurry biodegradation, Root zone Treatment

Vitrification

Technologies classification

Physicochemical

Table 1.

4

Physical Sedimentation,

Biological Tricking filters,

Floatation.

Kinetic Modeling for Environmental Systems

Solvent Extraction, Reverses osmosis Ultra & micro Filtration, Sorption/Ion Exchange, Coagulation/ Precipitation.

Attached growth on granular bio-filters, Activated sludge

Technology for preventing and control of environmental pollution

the overall system performance [5, 6]. Figure 1 illustrates the integration of

used to predict pollutant migration will be illustrated. The selection of

research and assessment models, in which the studied subsystems are characterized and the factors that affect their behavior are identified experimentally. Then models are used within the research efforts to interpret, extrapolate/interpolate, and optimize the collected data; the modeling results will be used to evaluate and rank the FEPs that affect the system. In assessment models, important FEPs are linked to identify the problem formulation and basic system description, and then conceptual and computational models are constructed, verified, and used [5–11]. For instance, the quantification of the effect of time on the pollutants migration in terrestrial, aquatic, and/or atmospheric subsystems is usually conducted by measuring the concentration of major pollutants at incremental time at different distances from the source. Experiments are run for specified time determined based on the temporal scale of the study. The collected experimental data are analyzed to quantify the processes that control the migration. This analysis might include the use of simple empirical, semiempirical, or mechanistic mathematical models that allow a clear understanding of the nature of the processes that affect the migration. In terrestrial subsystems, these processes might include percolation, retardation, biodegradation, advection, and hydrodynamic dispersion [8, 11]. In subsequent sections, the development of assessment models to support the decision-making process will be illustrated with special emphasis on the prediction of pollutant migration. In this respect, the iterative nature of the assessment modeling will be overviewed, the conceptual model will be introduced, and some conceptual models that could be

subsurface and surface water will be determined. In this case, different terrestrial and atmospheric exposure pathways to receptors, downstream the contamination source, were identified as main exposure routes. Figure 3 illustrates the main processes that can lead to pollutant migration or attenuation from a contaminant spill into different subsystems. The pollutants are assumed to be transported by percolation, surface runoff, and evaporation, and attenuation is assumed to occur as

Introductory Chapter: Development of Assessment Models to Support Pollution Preventive…

DOI: http://dx.doi.org/10.5772/intechopen.83822

Conceptual model to predict pollutant migration/attenuation from the source term into the surrounding environment.

Conceptual model to quantify the effect of continuous atmospheric discharge on the worker [14].

Conceptual model to quantify the effect of pesticide application on the environment [15].

Figure 3.

Figure 4.

Figure 5.

7

Figure 2.

Iterative nature of the modeling process and its relation with system-specific data.

## 3. Development of conceptual model for pollutant migration assessments

Conceptual model is defined as "A simplified representation of how the real system is believed to behave based on a qualitative analysis of field data" [11]. The development of a conceptual model starts with a clear determination of available information and knowledge gaps about the system. Subsequently, essential FEPs and their interactions in each subsystem are identified, and assumptions that were made to include or exclude any of these FEPs are highlighted based on the results of the research models [11]. Finally, flowcharts are used to describe the graphical relationship between different processes in different physical subsystems. It should be noted that the conceptual model could be imperfect if over- or undersimplification of the studied system were used, where over-simplification can lead to ineffective model with large uncertainties and under-simplification can lead to complex model that raises the project costs. Imperfect conceptual model could be resulted from incomplete problem identification/assessment context, wrong assumptions in developing the conceptual model, and poor identification of the important processes.

Conceptual models are usually constructed based on source-pathway-receptor analysis, where pollution sources are defined by investigating the driving forces and duration of the releases for each pollutant, the routes of pollutant transport between different physical subsystems are determined, and receptor exposure mechanisms and duration are identified [9, 13, 14]. Below are some examples that illustrate the construction of conceptual model for pollutant migration into different subsystems that could be developed to support the pollutant control and prevention decisionmaking process.

To characterize the extent of the contamination problems due to contaminant spill, there is a need to collect samples from potentially affected subsystems, that is, groundwater, surface water, air, and soil and subsoil. Sampling procedure should consider both the main pollutants and subsystem properties, for example, pollutant concentrations in different subsystems, water pH, velocity, wind velocity, etc. Characterization results will be analyzed within the research modeling efforts, and the results of this analysis will determine the complexity of the model. Based on these results, homogenous or nonhomogenous subsurface may be considered to estimate pollutant percolation and sorption, and the elimination or inclusion of biodegradation and aquifer recharge as sink or source for pollutants in the

Introductory Chapter: Development of Assessment Models to Support Pollution Preventive… DOI: http://dx.doi.org/10.5772/intechopen.83822

subsurface and surface water will be determined. In this case, different terrestrial and atmospheric exposure pathways to receptors, downstream the contamination source, were identified as main exposure routes. Figure 3 illustrates the main processes that can lead to pollutant migration or attenuation from a contaminant spill into different subsystems. The pollutants are assumed to be transported by percolation, surface runoff, and evaporation, and attenuation is assumed to occur as

#### Figure 3.

3. Development of conceptual model for pollutant migration

Iterative nature of the modeling process and its relation with system-specific data.

be noted that the conceptual model could be imperfect if over- or under-

Conceptual model is defined as "A simplified representation of how the real system is believed to behave based on a qualitative analysis of field data" [11]. The development of a conceptual model starts with a clear determination of available information and knowledge gaps about the system. Subsequently, essential FEPs and their interactions in each subsystem are identified, and assumptions that were made to include or exclude any of these FEPs are highlighted based on the results of the research models [11]. Finally, flowcharts are used to describe the graphical relationship between different processes in different physical subsystems. It should

simplification of the studied system were used, where over-simplification can lead to ineffective model with large uncertainties and under-simplification can lead to complex model that raises the project costs. Imperfect conceptual model could be resulted from incomplete problem identification/assessment context, wrong assumptions in developing the conceptual model, and poor identification of the

Conceptual models are usually constructed based on source-pathway-receptor analysis, where pollution sources are defined by investigating the driving forces and duration of the releases for each pollutant, the routes of pollutant transport between different physical subsystems are determined, and receptor exposure mechanisms and duration are identified [9, 13, 14]. Below are some examples that illustrate the construction of conceptual model for pollutant migration into different subsystems that could be developed to support the pollutant control and prevention decision-

To characterize the extent of the contamination problems due to contaminant spill, there is a need to collect samples from potentially affected subsystems, that is, groundwater, surface water, air, and soil and subsoil. Sampling procedure should consider both the main pollutants and subsystem properties, for example, pollutant concentrations in different subsystems, water pH, velocity, wind velocity, etc. Characterization results will be analyzed within the research modeling efforts, and the results of this analysis will determine the complexity of the model. Based on these results, homogenous or nonhomogenous subsurface may be considered to estimate pollutant percolation and sorption, and the elimination or inclusion of biodegradation and aquifer recharge as sink or source for pollutants in the

assessments

Kinetic Modeling for Environmental Systems

Figure 2.

important processes.

making process.

6

Conceptual model to predict pollutant migration/attenuation from the source term into the surrounding environment.

#### Figure 4.

Conceptual model to quantify the effect of continuous atmospheric discharge on the worker [14].

a result of sorption into the subsurface and biodegradation within surface water, groundwater, and geosphere.

To determine the worker dose in a radioactive waste incinerator facility during the planning phase for transition from batch to continuous operation, a conceptual model was constructed [14]. The pollutants are assumed to be transported through the air via advective-diffusive process, and the exposure means were determined to include inhalation of gaseous pollutants (which is the main exposure mean in that study), direct dermal exposure, and ingestion of contaminated water (Figure 4).

Generic conceptual model to quantify the effect of pesticide application on the environment is suggested by US EPA (Figure 5) [15]. The model represents terrestrial exposure pathways, where the pollutants (pesticide) are transported through the atmospheric and aquatic subsystems and were assumed to affect terrestrial receptors, that is, plants, invertebrates, and vertebrates. The exposure means included inhalation, dermal exposure, and ingestion with a detailed characterization of the dietary routes.

## 4. Computational representation of the conceptual model

The development of the computational model that represents accurately the conceptual model is a crucial task, where the accuracy of the obtained results will be used to judge if the modeling effort is enough to represent the system or there will be a need to acquire field data and develop an updated model (Figure 2). For a simple conceptual model, a simple empirical model could be used, as the sitespecific information is available and a more realistic model could be used [13]. The type of the mathematical representation of the conceptual model is defined during the problem formulation, and the selection of the appropriate model is bounded by [4, 11]:


flow (infiltration/flow rate, travel time, and average water velocity) and transport parameters (hydrodynamic dispersion, distribution, and retardation coefficient) for homogenous and nonhomogenous soil under saturated and vadose conditions.

Model use Parameters Model

DOI: http://dx.doi.org/10.5772/intechopen.83822

Soil sorptivity (S, m/d0.5), Soil dependent constant (A)

Introductory Chapter: Development of Assessment Models to Support Pollution Preventive…

Hydraulic gradient (i), Hydraulic conductivity (k, m/d)

Dimensionless time (t\*), Dimensionless depth (z\*), Change in volumetric water content as the wetting front passes

layer n (δθ, m<sup>3</sup> /m<sup>3</sup> ),

(Hn, m)

/d)

Porosity (n).

weight (m, g)

porosity (ε).

and (Kf, mg/g)

Soil density (ρ, kg/m<sup>3</sup>

Potential head while the wetting front passes through layer n,

Vadose zone thickness (d, m),

Effluent pore volume (u), Distance (L, m),

Mean pore water velocity (v, m/d).

Freundlich constant indicative of the relative sorption capacity (n)

Maximum sorbed as calculated by D-R isotherm (qm, mg/g), Energy of sorption estimated by D–R model (E, kJ/mol), Gas constant (R,8.314 J/mol K), Absolute temperature (T, K)

Mathematical models used to assess the migration in soil subsurface [5, 16–20].

), Soil

Concentration in the solution (C, ppm) at initial (i) and final (e) state, Solution volume (V, l), Soil

q tðÞ¼ <sup>1</sup>

q = ki

<sup>q</sup> <sup>=</sup> <sup>0</sup>:<sup>5</sup> <sup>t</sup>

t = dn/q

v = Ki/n

Di <sup>¼</sup> vL 8 U�1ffiffiffi U <sup>p</sup> <sup>j</sup>0:<sup>84</sup> � <sup>U</sup>�<sup>1</sup>ffiffiffi U <sup>p</sup> <sup>j</sup>0:<sup>16</sup> � �<sup>2</sup>

Kdi <sup>¼</sup> Cii�Cei Cii � � <sup>V</sup> m � � � <sup>1000</sup>

Rf <sup>¼</sup> <sup>1</sup> <sup>þ</sup> <sup>ρ</sup>ð Þ <sup>1</sup>�<sup>ε</sup>

Rf <sup>¼</sup> <sup>1</sup> <sup>þ</sup> <sup>ρ</sup>Kf

Rf <sup>¼</sup> <sup>1</sup> <sup>þ</sup> <sup>ρ</sup>RTqmE<sup>2</sup>

ln <sup>C</sup>þ<sup>1</sup> C � � <sup>C</sup> Cþ1 � � <sup>1</sup> C � �

<sup>θ</sup> Kdi

<sup>θ</sup><sup>n</sup> <sup>C</sup> <sup>1</sup>�<sup>n</sup> ð Þ <sup>n</sup>

<sup>θ</sup> exp RTln <sup>1</sup>þ<sup>1</sup> ð Þ <sup>=</sup><sup>c</sup>

2 2E<sup>2</sup> � �

t <sup>∗</sup> <sup>¼</sup> knt δθ Hnþ∑n�<sup>1</sup> ð Þ <sup>i</sup>¼<sup>1</sup> Zi

<sup>2</sup> St�0:<sup>5</sup> <sup>þ</sup> <sup>A</sup>

<sup>∗</sup>�2z∗<sup>þ</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffi <sup>t</sup>∗�2z<sup>∗</sup> ð Þ <sup>p</sup> <sup>þ</sup>8<sup>t</sup> ∗

� �þ<sup>1</sup> <sup>0</sup>:<sup>5</sup> <sup>t</sup>∗�2z∗<sup>þ</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffi <sup>t</sup>∗�2z<sup>∗</sup> ð Þ <sup>p</sup> <sup>þ</sup>8t<sup>∗</sup> � �þ<sup>z</sup> � �\*kn

> <sup>z</sup><sup>∗</sup> <sup>¼</sup> kn Hnþ∑n‐<sup>1</sup> <sup>i</sup>¼<sup>1</sup> ð Þ zi

∑<sup>n</sup>‐<sup>1</sup> i¼1 zi ki

Infiltration rate in homogenous soil, (q, m/d)

Flow rate in nonhomogenous soil (q, m/d)

Pollutant Travel time,

Water average velocity (v, m/d)

Hydrodynamic dispersion (Dl, m<sup>2</sup>

Distribution coefficient (kd) of element (i) assuming linear isotherm

Retardation coefficient (Rf) in vadose zone

isotherm

Table 2.

9

Retardation factor assuming Freundlich

Retardation factor assuming D-R

(t, d)

Flow rate in homogenous soil, (q, m/d)

The author would like to acknowledge Dr. A.A. Zaki, professor of nuclear chemical engineering at Atomic Energy Authority of Egypt, for the time and efforts

Acknowledgements

that he spent to review this work.

During the development of a mathematical representation, the studied system is usually divided into a subsystem. For the conceptual model presented in Figure 3, the system could be divided into source subsystem which describes the mobilization of the pollutant from the source, terrestrial migration, atmospheric transport, and receptors subsystems. Table 2 shows some simple models that could be used to develop a mathematical representation of pollutant migration in terrestrial compartment [5, 16–20]. This table presents models that could be used to estimate both


Introductory Chapter: Development of Assessment Models to Support Pollution Preventive… DOI: http://dx.doi.org/10.5772/intechopen.83822

#### Table 2.

a result of sorption into the subsurface and biodegradation within surface water,

4. Computational representation of the conceptual model

will be used to represent the system.

5. Homogenous and nonhomogenous system.

The development of the computational model that represents accurately the conceptual model is a crucial task, where the accuracy of the obtained results will be used to judge if the modeling effort is enough to represent the system or there will be a need to acquire field data and develop an updated model (Figure 2). For a simple conceptual model, a simple empirical model could be used, as the sitespecific information is available and a more realistic model could be used [13]. The type of the mathematical representation of the conceptual model is defined during the problem formulation, and the selection of the appropriate model is bounded by

1. System dimensions: decision should be made if one, two, and three dimensions

2. Nature of the boundary conditions: Source terms release assumptions should identify if the release is constant or variable throughout the time and space.

3. Steady state or time variant model: the system behavior is changing with time

6.Type of flow and transport process: the flow occurs via intergranular or fissure flow, and the transport is governed by advection or hydrodynamic dispersion.

During the development of a mathematical representation, the studied system is usually divided into a subsystem. For the conceptual model presented in Figure 3, the system could be divided into source subsystem which describes the mobilization of the pollutant from the source, terrestrial migration, atmospheric transport, and receptors subsystems. Table 2 shows some simple models that could be used to develop a mathematical representation of pollutant migration in terrestrial compartment [5, 16–20]. This table presents models that could be used to estimate both

4.Uncertainty management: probabilistic or deterministic approaches.

To determine the worker dose in a radioactive waste incinerator facility during the planning phase for transition from batch to continuous operation, a conceptual model was constructed [14]. The pollutants are assumed to be transported through the air via advective-diffusive process, and the exposure means were determined to include inhalation of gaseous pollutants (which is the main exposure mean in that study), direct dermal exposure, and ingestion of contaminated water (Figure 4). Generic conceptual model to quantify the effect of pesticide application on the environment is suggested by US EPA (Figure 5) [15]. The model represents terrestrial exposure pathways, where the pollutants (pesticide) are transported through the atmospheric and aquatic subsystems and were assumed to affect terrestrial receptors, that is, plants, invertebrates, and vertebrates. The exposure means included inhalation, dermal exposure, and ingestion with a detailed characterization

groundwater, and geosphere.

Kinetic Modeling for Environmental Systems

of the dietary routes.

[4, 11]:

8

or fixed.

Mathematical models used to assess the migration in soil subsurface [5, 16–20].

flow (infiltration/flow rate, travel time, and average water velocity) and transport parameters (hydrodynamic dispersion, distribution, and retardation coefficient) for homogenous and nonhomogenous soil under saturated and vadose conditions.

## Acknowledgements

The author would like to acknowledge Dr. A.A. Zaki, professor of nuclear chemical engineering at Atomic Energy Authority of Egypt, for the time and efforts that he spent to review this work.

Kinetic Modeling for Environmental Systems

References

33(2):588-596

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DOI: http://dx.doi.org/10.5772/intechopen.83822

[8] Abdel Rahman RO, Ibrahim HA, Abdel Monem NM. Long-term

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Radionuclide Migration in the Environment. IntechOpen: DOI: 10.5772/intechopen.76818. ISBN: 978-1- 78923-616-3. Available from: https:// www.intechopen.com/books/principle s-and-applications-in-nuclear-enginee ring-radiation-effects-thermal-hydra ulics-radionuclide-migration-in-the-e nvironment/introductory-chapter-safe ty-aspects-in-nuclear-engineering

[10] Guidance on risk assessment and the use of conceptual models for groundwater. Guidance Document No. 26. ISBN-13: 978-92-79-16699-0. 2010.

[11] McMahon A, Heathcote J, Carey M, Erskine A. Guide to Good Practice for the Development of Conceptual Models and the Selection and Application of Mathematical, National Groundwater & Contaminated Land Centre Report NC/ 99/38/2 (2001). ISBN: 1 857 05610 8

[12] Abdel Rahman RO. Preliminary evaluation of the technical feasibility of using different soils in waste disposal cover system. Environmental Progress & Sustainable Energy. 2011;30(1):19-28

[13] Barnthouse LW, Suter GW II, Guide for Developing Data Quality Objectives for Ecological Risk Assessment at DOE Oak Ridge, Operations Facilities, ES/ER/ TM-185/R1; Springfield, VA: National

DOI: 10.2779/53333

2009;149:143-152

Introductory Chapter: Development of Assessment Models to Support Pollution Preventive…

performance of zeolite Na A-X blend as backfill material in near surface disposal vault. Chemical Engineering Journal.

[2] Abdel Rahman RO, Elmesawy M, Ashour I, Hung Y-T. Remediation of NORM and TENORM contaminated sites–review article. Environmental Progress & Sustainable Energy. 2014;

[3] Chen Q, Han R, Ye F, Li W. Spatiotemporal ecological models. Ecological

Disposal Facilities, vol. 1. Vienna: IAEA;

[5] Abdel Rahman RO, El-Kamash AM, Zaki AA. Modeling the long term leaching behavior of 137Cs, 60Co, and152,154Eu radionuclides from cement-clay matrices. Hazardous Materials. 2007;145:372-380

[6] Drace Z, Mele I, Ojovan MI, Abdel Rahman RO. An overview of research activities on cementitious materials for radioactive waste management.

Materials Research Society Symposium Proceedings. 2012;1475:253-264. DOI:

[7] Abdel Rahman RO, Rakhimov RZ,

Cementitious Materials for Nuclear Waste Immobilisation. New York: Wiley; 2014. ISBN: 9781118512005. http://eu.wiley.com/WileyCDA/Wile yTitle/productCd-1118512006,subjec

Rakhimova NR, Ojovan MI.

10.1557/opl.2012

tCd-CH50.html

11

Informatics. 2011;6:37-43

[4] IAEA. Safety Assessment Methodologies for Near Surface

2004. ISBN: 92-0-104004-0

## Author details

Rehab O. Abdel Rahman Hot Lab. Center, Atomic Energy Authority of Egypt, Cairo, Egypt

\*Address all correspondence to: alaarehab@yahoo.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introductory Chapter: Development of Assessment Models to Support Pollution Preventive… DOI: http://dx.doi.org/10.5772/intechopen.83822

## References

[1] Abdel Rahman RO, Kozak MW, Hung Y-T. Radioactive pollution and control, Ch (16). In: Hung YT, Wang LK, Shammas NK, editors. Handbook of Environment and Waste Management. Singapore: World Scientific Publishing Co; Feb 2014. pp. 949-1027. DOI: 10.1142/9789814449175\_0016. Available from: http://www.worldscientific.com/ doi/abs/10.1142/9789814449175\_0016

[2] Abdel Rahman RO, Elmesawy M, Ashour I, Hung Y-T. Remediation of NORM and TENORM contaminated sites–review article. Environmental Progress & Sustainable Energy. 2014; 33(2):588-596

[3] Chen Q, Han R, Ye F, Li W. Spatiotemporal ecological models. Ecological Informatics. 2011;6:37-43

[4] IAEA. Safety Assessment Methodologies for Near Surface Disposal Facilities, vol. 1. Vienna: IAEA; 2004. ISBN: 92-0-104004-0

[5] Abdel Rahman RO, El-Kamash AM, Zaki AA. Modeling the long term leaching behavior of 137Cs, 60Co, and152,154Eu radionuclides from cement-clay matrices. Hazardous Materials. 2007;145:372-380

[6] Drace Z, Mele I, Ojovan MI, Abdel Rahman RO. An overview of research activities on cementitious materials for radioactive waste management. Materials Research Society Symposium Proceedings. 2012;1475:253-264. DOI: 10.1557/opl.2012

[7] Abdel Rahman RO, Rakhimov RZ, Rakhimova NR, Ojovan MI. Cementitious Materials for Nuclear Waste Immobilisation. New York: Wiley; 2014. ISBN: 9781118512005. http://eu.wiley.com/WileyCDA/Wile yTitle/productCd-1118512006,subjec tCd-CH50.html

[8] Abdel Rahman RO, Ibrahim HA, Abdel Monem NM. Long-term performance of zeolite Na A-X blend as backfill material in near surface disposal vault. Chemical Engineering Journal. 2009;149:143-152

[9] Abdel Rahman RO, Saleh HM. Introductory chapter: Safety aspects in nuclear engineering. In: Abdel Rahman RO, Saleh HM, editors. Principles and Applications in Nuclear Engineering: Radiation Effects, Thermal Hydraulics, Radionuclide Migration in the Environment. IntechOpen: DOI: 10.5772/intechopen.76818. ISBN: 978-1- 78923-616-3. Available from: https:// www.intechopen.com/books/principle s-and-applications-in-nuclear-enginee ring-radiation-effects-thermal-hydra ulics-radionuclide-migration-in-the-e nvironment/introductory-chapter-safe ty-aspects-in-nuclear-engineering

[10] Guidance on risk assessment and the use of conceptual models for groundwater. Guidance Document No. 26. ISBN-13: 978-92-79-16699-0. 2010. DOI: 10.2779/53333

[11] McMahon A, Heathcote J, Carey M, Erskine A. Guide to Good Practice for the Development of Conceptual Models and the Selection and Application of Mathematical, National Groundwater & Contaminated Land Centre Report NC/ 99/38/2 (2001). ISBN: 1 857 05610 8

[12] Abdel Rahman RO. Preliminary evaluation of the technical feasibility of using different soils in waste disposal cover system. Environmental Progress & Sustainable Energy. 2011;30(1):19-28

[13] Barnthouse LW, Suter GW II, Guide for Developing Data Quality Objectives for Ecological Risk Assessment at DOE Oak Ridge, Operations Facilities, ES/ER/ TM-185/R1; Springfield, VA: National

Author details

10

Rehab O. Abdel Rahman

Kinetic Modeling for Environmental Systems

Hot Lab. Center, Atomic Energy Authority of Egypt, Cairo, Egypt

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: alaarehab@yahoo.com

provided the original work is properly cited.

Technical Information Service, U.S. Department of Commerce; 1996

[14] Abdel Rahman RO. Preliminary assessment of continuous atmospheric discharge from the low active waste incinerator. International Journal of Environmental Sciences. 2010;1(2): 111-122

[15] EPA. Guidance for the Development of Conceptual Models for a Problem Formulation Developed for Registration Review. Available from: https://www.e pa.gov/pesticide-science-and-asse ssing-pesticide-risks/guidance-deve lopment-conceptual-models-problem. [Accessed: 28/11/2018]

[16] Abdel Rahman RO, Abdel Moamen OA, Hanafy M, Abdel Monem NM. Preliminary investigation of zinc transport through zeolite-X barrier: Linear isotherm assumption. Chemical Engineering Journal. 2012;185–186: 61-70

[17] Abdel Rahman RO. Performance assessment of unsaturated zone as a part of waste disposal site [PhD thesis]. Egypt: Nuclear Engineering Dep., Faculty of Engineering, Alexandria University; 2005

[18] Abdel Rahman RO, El Kamash AM, Zaki AA, El Sourougy MR. Disposal: A last step towards an integrated waste management system in Egypt. In: International Conference on the Safety of Radioactive Waste Disposal; Tokyo, Japan. IAEA-CN-135/81; 2005. pp. 317-324

[19] Gasser MS, El Sherif E, Abdel Rahman RO. Modification of Mg-Fe hydrotalcite using Cyanex 272 for lanthanides separation. Chemical Engineering Journal. 2017;316C:758-769

[20] Abdel Rahman RO, Ibrahim HA, Hanafy M, Abdel Monem NM. Assessment of synthetic zeolite NaA-X as sorbing barrier for strontium in a

radioactive disposal facility. Chemical Engineering Journal. 2010;157:100-112

Section 2

Pollution Prevention

and Controls

13

Section 2
