Universidad del Turabo

Phytoremediation Dynamic Model For Environmental Management

By

Rafael R. Canales-Pastrana B.S., Physics Applied to the Electronics, University of Puerto Rico at Humacao

M.S., Physics, University of Puerto Rico at Río Piedras

DISSERTATION

Submitted to the School of Science and Technology in partial fulfillment of the requirements for

the degree of Doctor of Philosophy

in Environmental Science

(Management Option)

Gurabo, Puerto Rico

May, 2013

ii

Universidad del Turabo

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

2/15/2013

Phytoremediation Dynamic Model for Environmental Management

Rafael R. Canales-Pastrana

Approved: Eddie Laboy Nieves, Ph.D. Oscar N. Ruiz Ocasio, Ph.D. Supervising Professor Supervising Professor Angel Rivera, Ph.D. Marlio Paredes, Ph.D. Member Member Santander Nieto, Ph.D. Elio Ramos, Ph.D. Member Member Carlos Olivo,PhD Teresa Lipsett, PhD Associate Dean Dean

© Copyright 2013 Rafael R. Canales-Pastrana. All Rights Reserved.

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Dedications

To God, for providing me the courage to not give up, family and friends that were

my support on this venture specially; to my inner circle family my wife Marie T. López

Rohena, my kids Ricardo R. Canales López and Dana I. Canales López, which are my

happiness.

Also, I want to include three of my mentors: Dr. Fredy Zypman for being my

father on scientific research, Dr. Eddie N. Laboy Nieves for being my mentor and a

benchmark in the academic area, and Dr. Oscar N. Ruiz Ocasio for being my model as a

scientific researcher.

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Acknowledgments

Completing a doctoral degree requires a strong commitment, effort and support;

for that I would like to thank to my committee members: Dr. Eddie N. Laboy Nieves, Dr.

Oscar N. Ruiz Ocasio, Dr. Elio Ramos, Dr. Ángel Rivera Collazo, Dr. Santander Nieto

and Dr. Marlio Paredes. Their wisdom provided me the right advice to follow the

pathway to true scientific endeavors. Also, to all graduated professors that shared their

knowledge with me.

I would like to thank the faculty and administration of the Inter American

University, Bayamon Campus for providing me the economic support and

encouragement to finish this degree. I am especially grateful to Ana M. Feliciano

Delgado, Dr. Bert Rivera Marchand and Dr. Iván Ferrer Rodríguez, for their

unconditional support and for reviewing this manuscript at early stages.

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Vita

Mr. Rafael R. Canales Pastrana was born in San Juan, Puerto Rico in June

1974. He holds a Bachelor Degree in Physics Applied to the Electronics from University

of Puerto Rico Humacao Campus and Master Degree in Theoretical Physics from

University of Puerto Rico Rio, Piedras Campus.

He has been working in the academia for the past 15 years. During the last 12

years he has worked at the Inter American University, Bayamon Campus, in academia

as well as a quasi-administrative position. Currently he is National Science Foundation

PI under Chemical, Bioengineering, Environmental, and Transport Systems (CBET)

program with the proposal entitled: Development of a Chloroplast Chelator System for

Mercury Phytoremediation.

vi

Table of Contents

page

List of Tables .............................................................................................................. viii

List of Figures .............................................................................................................. x

Abstract .............................................................................................................. xv

Resumen in Spanish ................................................................................................... xvii

Chapter One. Introduction .......................................................................................... 1

1.1. Statement of the Problem ................................................................................ 5

1.2. Hypotheses ...................................................................................................... 12

1.3. Rationale ......................................................................................................... 13

1.4. Intellectual Merit ............................................................................................... 15

1.5. Broader Impacts .............................................................................................. 15

Chapter Two. Literature Review ................................................................................ 17

Chapter Three. Methods ............................................................................................ 22

3.1. Theoretical Background ................................................................................... 22

3.2. Research Methodology .................................................................................... 24

3.3. Model Construction and Validation................................................................... 27

Chapter Four. Results ................................................................................................ 38

4.1. General Findings ............................................................................................. 38

4.2. Quantitative Equivalence between PDM and Experimental Data ..................... 42

4.3. Sensitivity Analysis for the Calibrated Variables............................................... 45

4.4. Phytoremediation Constringent Factor Determination ...................................... 52

Chapter Five. Discussion ............................................................................................ 55

5.1. Concentration Response and Performance ..................................................... 55

5.2. Plant Type Determination (species or genetically modified) ............................. 61

vii

5.3. Capability of the PDM to Model different Phytoremediation Systems ............... 67

5.4. General Discussion .......................................................................................... 71

5.5. Concluding Remarks ....................................................................................... 75

5.6. Limitations of the Study .................................................................................... 76

Literature Cited ............................................................................................................ 78

viii

List of Tables

page

Table 3.01. Differential equation system which describes PDM. ............................... 31

Table 3.02. Auxiliary variable categorization and base scenario values. ................... 36

Table 4.01. Data sets used for graphical validation. ................................................. 40

Table 4.02. Descriptive statistical analysis for volatilized and cumulative mercury

concentration (µHg) by approach. (standard deviation (s); coefficient

of variation (CV)) .................................................................................... 41

Table 4.03. Sign test for a confidence level of 95%, testing: median = 10.00

versus median ≠ 10.00 ........................................................................... 45

Table 4.04. Krustal-Wallis statistical test of the sensitivity analysis ........................... 53

Table 4.05. Grouping information using Tukey Method. ............................................ 54

Table 4.06. Individual 95% confidence interval based on pooled standard

deviation (sp) .......................................................................................... 54

Table 5.01. Calibrated auxiliary variable and values, for pLDR-merAB3’UTR

transgenic line. ...................................................................................... 61

Table 5.02. Sign test for a confidence level of 95%, testing: median = 10.00

versus median ≠ 10.00 ........................................................................... 63

Table 5.03. Calibrated auxiliary variable values in µg Hg/(d* µg Hg in Socks) for

pLDR-merAB and pLDR-merAB3’UTR transgenic lines. ........................ 64

Table 5.04. Auxiliary variables values to model different phytoremediation

process. ................................................................................................. 67

Table 5.05. Auxiliary variable categorization and base scenario values. ................... 69

ix

Table 5.06. Sign test for a confidence level of 95%, testing: median = 10.00

versus median ≠ 10.00 ........................................................................... 70

x

List of Figures

page

Figure 1.01. Mass circulation between biotic and abiotic components. ...................... 4

Figure 1.02. Global present-day mercury concentration balance (Mg) as

represented in an atmospheric model with coupled surface

reservoirs (GEOS-Chem). Blue arrows show primary and legacy

sources of mercury to the atmosphere from long-lived deep

reservoirs. Red arrows show the fate of mercury in surface (ocean,

land, snow) reservoirs: recycling to the atmosphere or incorporation

into more stable reservoirs (deep ocean, soils). Black arrows show

deposition and redox fluxes. Green arrows show processes not

explicitly modeled in GEOS-Chem. Order-of-magnitude residence

times in individual reservoirs are also shown (Corbitt et al. 2011). ......... 7

Figure 1.03. Mercury deposition fluxes, obtained by GEOS-Chem model from

preindustrial and present day. Numbers in the panels annual total

(Smith-Downey et al. 2010).................................................................... 8

Figure 1.04. National total mercury wet deposition during 2009 ................................. 9

Figure 1.05. Basic schematic representation of plant physiology, which

represents the phytoremediation process. ............................................. 16

Figure 2.01. The dynamic model for uptake and translocation of contaminant

from soil-plant ecosystems (UTCSP) constructed using STELLA

(Ouyan 2008). ........................................................................................ 21

Figure 3.01. Distinction between the environment and the system to be modeled;

using a bathtub classical example on system dynamic approach

xi

representing the three scenarios: (A) physical situation to be

modeled, (B) pictographic model representation and (C) the

mathematical representation in terms of differential equations ............... 23

Figure 3.02. Sequences of tobacco plant compartment based on plant anatomy

and physiology implemented in the Plant Kinetic Model (Sundberg

et al. 2003). ............................................................................................ 26

Figure 3.03. Dynamic structure diagram for the Phytoremediation Dynamic

Model (PDM), in which the system has been divided in the

compartments to be considered. The compartments can be

classified as above or below the ground. The (A) compartment

represents the soil-plant interaction at the root zone, which is the

below the ground section involving two stocks: soil and root. The

above ground segment; are composed by three stocks: (B) shoots,

(C) leaf, and (D) atmosphere. ................................................................ 28

Figure 3.04. (A) The Forrester Diagram schematic representation of the

Phytoremediation Dynamic Model. (B) The differential equation

system of the phytoremediation process. ............................................... 30

Figure 3.05. Volatilization data by genetically modified tobacco plant on

contaminated soil with 100 µM of HgCl2 (Adapted from Hussein et

al. 2007)................................................................................................. 35

Figure 3.06. Schematic representation of stock (level variables) and flow model

to obtain the cumulative volatilized mercury, using experimental

data. ...................................................................................................... 36

Figure 3.07. Comparison between experimental data and PDM. (A) Volatilized

µg Hg. (B) Cumulative volatilized µg Hg. ............................................... 37

xii

Figure 4.01. Regression fit analysis between experimental data and PDM,

showing the prediction (PI) and confidence (CI) intervals for

cumulative mercury concentration.......................................................... 43

Figure 4.02. Box plot comparison of experimental data, model and the difference

between experimental data and the model for cumulative mercury

concentration. The outliers are represented by asterisk (*). .................. 44

Figure 4.03. Comparison of mean and confidence interval between the

experimental data and PDM................................................................... 46

Figure 4.04. Sensitivity analysis of average cumulative volatilized mercury by

variables. ............................................................................................... 47

Figure 4.05. Response as function of fraction, according to the different scenario:

(1) sensitivity analysis of average cumulative volatilized mercury, (2)

Mean confidence interval of 95% of the absolute value of the

difference between the response and base scenario. ............................ 48

Figure 4.06. Response as function of extraction, according to the different

scenario: (1) Sensitivity analysis of average cumulative volatilized

mercury, (2) Mean confidence interval of 95% of the absolute value

of the difference between the response and base scenario. .................. 49

Figure 4.07. Response as function of translocation, according to the different

scenario: (1) Sensitivity analysis of average cumulative volatilized

mercury, (2) Mean confidence interval of 95% of the absolute value

of the difference between the response and base scenario. .................. 50

Figure 4.08. Response as function of incorporation, according to the different

scenario: (1) sensitivity analysis of average cumulative volatilized

mercury, (2) mean confidence interval of 95% of the absolute value

of the difference between the response and base scenario. .................. 51

xiii

Figure 4.09. Mean confidence interval of 95% of the absolute value of the

difference between the response and base scenario by factor and

treatment. .............................................................................................. 52

Figure 5.01. Soil contaminant concentration gradient curve to assess the effects

in the phytovolatilization system. (A) Cumulative mercury in the root

as function of initial soil contaminant concentration. (B) Cumulative

volatilized mercury as function of initial soil contaminant

concentration. ........................................................................................ 57

Figure 5.02. Percentage of mercury removal as a function of initial soil

contaminant concentration. (A) From 10 µM to 100 µM, with an

increment of 10 µM. (B) 100 µM ± 5%................................................... 59

Figure 5.03. Behavioral analysis as a function of contaminant soil concentration

value of 100 µM and 200 µM. (A) Cumulative volatilized mercury.

(B) Percentage of total mercury removed. .............................................. 60

Figure 5.04. Comparison between experimental data and PDM, for the

cumulative volatilized mercury by pLDR-merAB3’UTR transgenic

line. ........................................................................................................ 63

Figure 5.05. Regression fit analysis between experimental data and PDM, for the

pLDR-merAB3’UTR transgenic line; showing the prediction (PI) and

confidence (CI) intervals for cumulative mercury concentration. ............ 64

Figure 5.06. Performance comparison between transgenic lines. (A) Cumulative

volatilized mercury. (B) Percentage of total mercury removed. ............. 66

Figure 5.07. The distribution of perchlorate amended in leaf and depletion from

nutrient solution (Adapted from Sundberg et al. 2003). .......................... 68

xiv

Figure 5.08. Comparison between experimental data (Sundberg et al. 2003) and

PDM, for the distribution of perchlorate amended in leaf and

depletion from nutrient solution. ............................................................. 70

Figure 5.09. Regression fit analysis between experimental data and PDM,

showing the prediction (PI) and confidence (CI) intervals for

cumulative mercury concentration, for the distribution of perchlorate

with initial concentration in solution of 25 ppm. ...................................... 71

xv

Abstract

Rafael R. Canales-Pastrana (Ph.D., Environmental Science)

Phytoremediation Dynamic Model for Environmental Management (February 2013)

Abstract of a doctoral dissertation at the Universidad del Turabo.

Dissertation supervised by Dr. Eddie N. Laboy Nieves and Dr. Oscar N. Ruiz Ocasio

No. of pages in text: 87.

Global contamination has increased in post-industrial times and with it the chemical

degradation of the environment. Different cleanup techniques of chemical contaminants

have been developed, being phytoremediation one of the most viable and cost effective

processes. However, this technique has not been fully commercialized, because of

concerns with the system’s performance; previously different mathematical approaches

were implemented to characterize phytoremediation systems, such as: differential

equation solution sets, statistical correlation and system dynamics approach. In this

study, the Phytoremediation Dynamic Model (PDM) was developed, and the system

dynamic approach was used to simulate the classical plant structure and the interaction

of plants with the polluted media. This model was tested and assessed using peer

review experimental data, evidencing its capability to mimic phytoremediation processes

(phytovolatilization, phytoextraction) in different media (soil, solution) and pollutant

(mercury chloride, perchlorate), obtaining more than 95 % of correlation. Also, it is

consistent with previous research establishing the extraction process as a constringent

factor for this cleanup technique. The differential equations system which describes the

model includes a comprehensive parameter which captures plant bioavailability

dependence in the pollutant-media interaction; this has not been previously found in the

xvi

literature. The implementation of PDM in the different phytoremediation systems

provides knowledge about: pollutant-media-plant interaction, pollutant concentration and

flow rate through the plant. This information offers the opportunity to have quantitative

parameters to determine which phytoremediation system is adequate according to its

performance in a specific scenario.

xvii

Resumen

Rafael R. Canales-Pastrana (Ph.D., Ciencias Ambientales)

Phytoremediation Dynamic Model for Environmental Management (Febrero 2013)

Resumen de una disertación doctoral en la Universidad del Turabo.

Disertación supervisada por: Dr. Eddie N. Laboy Nieves y Dr. Oscar N. Ruiz Ocasio

Núm. de página en texto: 87.

En tiempos post industriales la contaminación a nivel global ha aumentado,

exacerbando la degradación química del ambiente. Para atender esta situación se han

desarrollado diferentes técnicas de limpieza, siendo la fitorremediación el proceso más

viable y costo efectivo. Sin embargo, esta técnica no ha sido implementada

comercialmente a su máxima capacidad, por preocupaciones con relación a su

desempeño; se han aplicado diferentes metodologías matemáticas para caracterizar

este sistema, tales como: sistemas de ecuaciones diferenciales, correlaciones

estadísticas y sistemodinámica. En esta investigación se desarrolló el Modelo Dinámico

de Fitorremediación (MDF), aplicando la sistemodinámica al modelo estructural clásico

de las plantas, con el propósito de simular las interacciones entre la planta y el medio

contaminado. Este modelo ha sido evaluado, probado y contrastado utilizando datos

experimentales publicados en revistas arbitradas, evidenciando que tiene la capacidad

de representar los procesos de fitorremediación (fitovolatilización, fitoextracción) en

diferentes medios (suelo, solución) y contaminantes (cloruro de mercurio, perclorato),

obteniendo más de un 95% de correlación. También, es consistente con las

investigaciones previas, las cuales establecen que el factor limitante de la técnica es el

proceso de extracción. El sistema de ecuaciones diferenciales que describen el modelo

xviii

incluye un parámetro general que captura la biodisponibilidad del contaminante para la

planta, en función de la interacción del contaminante con el medio; en la literatura no se

ha encontrado un parámetro similar. Al implementar MDF a los diferentes sistemas de

fitorremediación, el mismo provee información sobre: la interacción contaminante-

medio-planta, concentración y flujo del contaminante a través de la planta. Estos datos

permiten tener una evaluación cuantitativa para discriminar cuál es el sistema

fitorremediador más adecuado para cada escenario al modelar su desempeño.

1

Chapter One

Introduction

Environmental management requires an interdisciplinary approach which

considers the broad range impacts of anthropogenic factors to nature and enhances the

spatial and temporal analysis of human activities (Rizzo et al. 2006; Raymond et al.

2010; Polasky et al. 2011). As a discipline it has different approaches according to the

governmental involvement, since co-management partnerships (Plummer and

FitzGibbon 2004), attend the necessity of developing strategies or policies to assess the

environmental impact in sustainable terms (Boulanger and Bréchet 2005). Another

focus to tackle environmental management issues are through environmental ethics by

dealing with societal factors; providing different perspectives to examine biocentrism

such as: land ethic, deep ecology and social ecology including environmental justice

(Desjardins 2006).

As a science discipline, environmental management has standardized protocols

for the evaluation, performance and reporting of environmental procedures applicable for

both the private and public sector. For instance, the Environmental Management

System (EMS) focuses on the environmental dimension throughout the continuous

assessment based on total quality management approach (Marazza et al. 2010). It is a

helpful strategy for the decision-making process, because it considers cost-benefits,

eco-efficiency and performance (Jasch 2006). EMS is based on the Plan, Do, Check,

Act (PDCA) Cycle: (1) P: Plan activities according to priorities; define policies, goals,

targets and rules; (2) D: Implement the planning activities under the chosen rules; (3) C:

Verify the results, with appropriate monitoring; (4) A: According to the results review or

reaffirm priorities, goals, policies and rules (Marazza et al. 2010). Once, rules and

policies have been established, a risk communication is needed in order to satisfy EMS

2

approach. This action is considered as an important step in the risk management

process which needs to be comprehensive and accessible by making the information

available to the society at large of the society (Takeuchi et al. 2012).

In the case of trace elements pollution, the implementation of the PDCA

approach should be a priority to environmental scientist; particularly heavy metals (HM)

are not easily degraded, rather they are bioaccumulated (Piorrone and Mahaffey 2006;

Sardans et al. 2010; Pezzarossa et al. 2011). HM are key indicators of pollution since

the Industrial Revolution, which has exacerbated their accumulation in natural cycle

stock (level variables), increasing their residence time which increases their intrusion

probability in the food web (EC 2010). The HM frequently found in contaminated sites

are: Cd, Cr, Cu, Pb, Hg, Ni and Zn (WCED 1987; Henry 2000; Renberg et al. 2009), but

they can be transformed by microorganism interactions into a more bioavailable forms

like methyl and dimethyl compounds (Wood 1974; Ridley et al. 1977). An adequate

concentration of several HM is crucial in maintaining natural cycles and for the wellbeing

of the environment (Beolchini et al. 2011). The most evident example is in the soil-plant

interactions, where Mn, Fe, Ni, Cu, Zn and Mo promote positive synergies (Sarma

2011), while Hg, Pb, Bi, Cr, Sn and Ag can be dangerous to different species and crops

(Gardiner and Miller 2004; Pezzarossa et al. 2011). Some HM are toxic to humans; for

instance the exposure to mercury inflicts irreversible effects to the human body, hence is

a threat to public health (Henry 2000; Shafaghat et al. 2012).

Under natural conditions the environment recycles mass among its constituents,

maintaining the total amount of components approximately constant throughout different

cycles, as depicted in Figure 1.01, for the interaction between biotic and abiotic

components (EC 2010). The biotic section can be subdivided according to the

3

interaction on the different trophic levels, while the abiotic component categorizes each

sub cycle according to the media (air, water, soil) in which the species transformations

occurs. The total mass contribution of each level can be divided into natural and

anthropogenic contributions (EC 2010). The boxes in biotic and abiotic cycles represent

stock (level variables) for each compartment with its respective interaction. The mass

on each stock has their own residence time, providing the opportunity for degradation

and transformation.

Mercury was taken as a key example, according to public health relevance. The

global mercury budget has increased 3.3 times in post-industrial times which can be

ascribed to the exploitation of heavy metals (gold and silver) and coal burning (Strode et

al. 2009; Smith-Downey et al. 2010; Corbitt et al. 2011). This exploitation was focused

on the production of goods and services for humankind, without taking in to account the

effects on the dynamics of nature cycles (Renberg et al. 2009). Urbanization is another

activity which increases air pollution, releasing significant amounts of particulate matter

including heavy metals, to the atmosphere (Gunawardena et al. 2012). Those actions

affect the level of contaminant concentration on each stock, increasing residence time

and promoting a lag in the circulation dynamics.

Oceans characterize this situation receiving pollution from natural and

anthropogenic sources, having a tendency of biomagnifying along the food web (Okuku

and Peter 2012). This creates a situation that requires the attention of environmental

scientists and decision makers, because it provides a new synergistic opportunity of

interactions between mass cycles and its constitutive sections, enhancing the interaction

between biotic and abiotic sub-cycles (Renberg et al. 2009). As heavy metal pollution

has been increasing globally, more efficient and precise techniques are emerging to

4

characterize their environmental influence and implementation of impact minimization

techniques are occurring (O’Connell et al. 2008; Sundaray et al. 2010). Among these

techniques are implementations of geochemical analysis, multivariate analysis, and

ordinal logistic regression (Twarakavi and Kaluarachchi 2005; Yongming et al. 2006;

Sundaray et al. 2010).

Environmental managers have the responsibility to inform regulatory agencies

and the general public about the environmental issues they deal with. This is mainly

characterized by the construction of mathematical and graphical models, maps

exhibiting multivariate sequential probabilities, and mapping the heavy metal dispersal

based on background information (Smith-Downey et al. 2010). These components are

crucial to understand their possible interactions, the establishment of the final stage

goal, and the evaluation procedure on the remediation process (Carlson et al. 2001;

Biotic

Bacteria

Plants and

Others

Animals

Abiotic

Soil

Air

Water

Figure 1.01: Mass circulation between biotic and abiotic components.

5

Renberg et al. 2009; Polasky et al. 2011). In the contemporary society, the integration of

information technology is a state of the art approach to divulge that information (Carlson

et al. 2001). An example of this integration of mathematical and modeling approach to a

visualization strategy is the geographic information system (GIS) which provides a

comprehensive tool for the decision making process (Costanza and Voinov 2004).

These kinds of approaches have been implemented to determine the environmental

hazard index, a heavy metal risk parameter linked to a specific site location map, which

is calculated using the joint probability of each heavy metal and the characteristic of

each point location (Franco et al. 2006).

1.1. Statement of the Problem

Mercury contamination is a global concern, because it poses significant

environmental health issues due to its atmospheric dispersal (Piorrone and Mahaffey

2006; Newland et al. 2008). Its most common form is mercury sulfide (HgS) or cinnabar.

Natural emissions come from volcanoes, degassing Earth’s crust and evaporation from

water bodies (Henry 2000). Anthropogenic emissions represent 75% of the total

atmospheric mercury contamination, mainly from industrial coal combustion (44%),

hazardous waste incineration (33%), and manufacturing process (Godish 2004).

Global mercury circulation is leading by atmospheric emissions (EC 2010). Atmosphere

washout by precipitation or dry deposition ultimately will settle on sediments of water

bodies. These depositions initiate the regional circulation process, which can involve

mercury transformation by the action of methylated anaerobic bacteria species (Henry

2000). The interaction of mercury species on air, water and sediments are dynamic.

Figure 1.02 represents current global mercury budget which coupled with atmospheric

model with surface reservoirs (water, soil and sediments), showing the dynamic

6

interactions between its constitutive and the residence time (Corbitt et al. 2011). This

cycle governs the mercury concentration on each stock (air, water, sediment) and

exemplifies the existence of the different mercury species. Also, a mechanistic global

model of soil mercury storage and emission has been developed (Smith-Downey et al.

2010). The model considers deposition and re-suspension of mercury; Figure 1.03

shows comparison map between mercury deposition on present days and preindustrial

time (Smith-Downey et al. 2010). In 1996, the National Atmospheric Deposition

Program incorporated the Mercury Deposition Network, which includes over 100

collection points in United States and Canada (NADP 2009). The total mercury wet time

(Smith-Downey et al. 2010). In 1996, the National Atmospheric Deposition Program

incorporated the Mercury Deposition Network, which includes over 100 collection points

in United States and Canada (NADP 2009). The total mercury wet deposition in 2009

across the USA is shown in Figure 1.04, where a plume of mercury wet deposition on

the southeast and two hotspots on the west are well identified. Wet depositions

enhance the time that mercury can reach water bodies promoting bioavailability (Pérez-

Sanz et al. 2010). The increase of mercury in one stock in the cycle is an environmental

situation that should to be managed, especially interrupting the accumulation process in

sediments and soils.

Mercury is a heavy metal without a known environmental function in soil-plant

interaction besides has bioaccumulation capacity (Gardiner and Miller 2004). It exists in

ionic, organic and inorganic forms in the environment (Piorrone and Mahaffey 2006).

The low vapor pressure of elementary mercury facilitates its global dispersal, by the

disproportionate reaction Hg2+2 ↔ Hg+2 + Hg0 (Wood 1974). Aerobic microorganisms

can push the equation to the right hand side, transforming Hg+2 onto the more soluble

7

Figure 1.02: Global present-day mercury concentration balance (Mg) as

represented in an atmospheric model with coupled surface reservoirs

(GEOS-Chem). Blue arrows show primary and legacy sources of

mercury to the atmosphere from long-lived deep reservoirs. Red arrows

show the fate of mercury in surface (ocean, land, snow) reservoirs:

recycling to the atmosphere or incorporation into more stable reservoirs

(deep ocean, soils). Black arrows show deposition and redox fluxes.

Green arrows show processes not explicitly modeled in GEOS-Chem.

Order-of-magnitude residence times in individual reservoirs are also

shown (Corbitt et al. 2011).

8

Figure 1.03: Mercury deposition fluxes, obtained by GEOS-Chem

model from preindustrial and present day. Numbers in the panels

annual total (Smith-Downey et al. 2010).

form, HgS (Wood 1974; Clever et al. 1985). Anaerobic bacteria in sediments can

convert HgS to methylmercury and di-methylmercury, the most toxic form of this metal

(Ridley et al. 1977). The Hg species on sediments are bioavailable by plant and algae

fixation on the tissue, promoting the dispersion through the trophic web.

All mercury compounds are toxic to animals and plants at different exposure

levels. The health effect of elemental mercury at low environmental levels is unknown

but, at very high concentrations causes severe lung damage. Inorganic and organic

mercury compounds are irritating to the digestive system and cause damage to the

nervous system (CDC 2009). Organomercurial compounds have the capacity of binding

with lipids, promoting biomagnification processes within the trophic web. Different

9

Figure 1.04: National total mercury wet deposition

during 2009 (NADP 2009).

national and international environmental agencies have standardized exposure level

(EPA 1997). The maximum exposure for mercury vapor is 0.2 µg/m3 and 0.3 µg/kg/day

of methylmercury, by ingestion (MRL-ATSDR 2008). As an international agency, the

Food and Agricultural Organization of the United Nations reduced the prior dietary level

of 3.3 µg per kg to 1.6 µg per kg of body weight per week. That is a clear concern

worldwide regarding about mercury exposure (FAO 2003).

Consequently, cleanup of contaminated soils is one of the most important

environmental management, economic and public health issues. Chemical degradation

alone affects around 12% of two billion hectares of degraded soil worldwide (Bini 2010).

The European Union established seven environmental strategies guided for preventing

further soil degradation and restoring deteriorated soils at levels that enable the current

necessity use in which the cost implications are considered (EC 2006). Besides the risk

10

of water body contamination by soil washout runoff, there is also the risk of plants

growing on contaminated soils, which then extract and translocate pollutants

(McLaughlina et al. 1999; Mapanda et al. 2005). This situation can be exemplified

analyzing the capability of vegetables to accumulate heavy metals (Cui et al. 2004;

Mapanda et al. 2005).

Environmental scientists have developed different in-situ and ex-situ techniques

as remediation and cleanup technologies including flushing, chemical

reduction/oxidation, excavation and capping, and stabilization and solidification

(Kärenlampi et al. 2000; Hinton and Veiga 2001; Wu 2010). Excavation and capping is

the most commonly used procedure and has an estimate price of $2.5 million/hectare

treated (Bini 2010). Soil remediation methods for heavy metals contamination, are

environmentally invasive, expensive and inefficient, especially when applied to large

areas (Kärenlampi et al. 2000; Meers et al. 2008).

In response to this inefficiency, it is necessary to achieve a more cost-efficient

procedure for large scale cleanup. Promise is found on the implementation of a non-

traditional cleanup technique known as bioremediation: the use of living organisms (i.e.

bacteria, algae, fungi and plants) to extract or confine contaminants from the

environment (Fulekar and Sharma 2008; Wu et al. 2010). Cleanup techniques that

employ plants (phytoremediation) are considered a viable emerging technology to

cleanup trace elements (Henry 2000; Singh et al. 2003; Jadia and Fulekar 2009).

Phytoremediation has been promoted in Canada as aesthetically pleasing, solar driven,

and a passive technique to clean up metals, pesticides and hydrocarbons on engineered

wetlands (Zang et al. 2010). Also, the technique enhanced by biosolids has been

evaluated as landfill covers (Kim and Owens 2010). Some plants, like hydrophytes,

11

have intrinsic cleanup capabilities, but their efficacy varies significantly between species

(Xiao-bin et al. 2007; Lafabrie et al. 2010; Zornoza et al. 2010, Sarma 2011). To attain a

higher efficiency on heavy metal extraction, plants can be genetically modified to

increase their phytoremediation potential (Wu et al. 2010; Harfouche et al. 2011, Sarma

2011). Examples of this approach include the modification of Arabidopsis thaliana,

Nicotiana tabacum and Liriodendron tulipifera with the insertion of merA and merB, two

bacterial genes employed to increase the mercury remediation potential (Heaton et al.

1988; Rugh et al. 1996; Krämer 2005; Hussein et al. 2007; Harfouche et al. 2011).

Phytoremediation can be sub-divided into phytodegradation or

phytotransformation, phytovolatilization, phytoextraction, rhizofiltration and

phytostabilization (EPA 2000, Sarma 2011). Phytodegradation breaks down

contaminants as a consequence of the enzyme production by the plant root.

Phytovolatilization is the uptake of a contaminant; it’s transformation by metabolic

reactions, and later release through transpiration. In Phytoextraction (or

phytoaccumulation) plants uptake and translocate the contaminant from the root to

above ground tissues. This technique has been tested ex-situ exploring the capability of

different plant species to cleanup biosolids contaminated with mercury (Lomote et al.

2010). Rhizofiltration is the absorption or adsorption of the contaminant by the plant

root, while phytostabilization involves immobilization of contaminants in the root zone

(Pueke and Rennenberg 2005; TIP-EPA 2008). The main difference between each

process is contaminant interaction and the metabolic pathway. The complex interactions

between plant roots and the microbial community can facilitate the uptake. The

performance of this phytoremediation process has been considered a highly site-specific

12

technology (Henry 2000; Sorkhoh et al. 2010) considering the wide variation of

contaminants found in the same site.

The use of these phytoremediation methods can cost less than one tenth of the

price of conventional techniques (Krämer 2005; Jadia and Fulekar 2009; Bini 2010) and

are environmentally friendly. At a large scale, phytoremediation is a cost effective

cleanup alternative, and provides the possibility to recover heavy metals from plant

tissue (Wu et al. 2010). This is a promising characteristic, especially if the collected

quantity is economically feasible to recycle, which discourages extraction. The

interaction between different contaminants and soil properties affects the

phytoremediation process (Israr et al. 2010; Lafabrie et al. 2010; Wang et al. 2010,

Sarma 2011). The biggest drawbacks of this technology are: (1) metal bioavailability

within the rhizosphere, (2) uptake rate of metal by roots, (3) proportion of metal “fixed”

within the roots, (4) rate of xylem loading/translocation to shoots, and (5) cellular

tolerance to toxic metals (EPA 2000; FRTRb 2006, Sarma 2011). The first four pointed

drawbacks can be answered implementing a model for a phytoremediation system.

1.2. Hypotheses

This dissertation is focused on development a comprehensive phytoremediation

dynamic model (PDM) capable of determining the internal flow rate dynamic of mercury

and the concentration at each physiological stock also, through a sensitive comparison

analysis of the different variables to determine the constringent process on

phytoremediation. For the completion of the PDM, three specific situations were

identified and their respective hypotheses described:

Situation 1: Development of a schematic representation of the PDM transport

process for mercury remediation to identify the correspondent parameters according to

13

the literature, the relevant processes on phytoremediation dynamics, and evaluating the

effects of different parameter interactions for its possible inclusions.

Hypothesis 1: The schematic representation of the phytoremediation dynamic

transport process for mercury remediation will behave in the same way as the

experimental data.

Situation 2: Validation of the phytoremediation dynamic transport process for

mercury remediation using published data in peer-reviewed journals.

Hypothesis 2: The numerical results on time evolution of the model present a

numerical compatibility when compared with the data published.

Ho: The mean of difference between the model and the experimental data, will

be equal to 10 units, with a confidence of 95%.

Ha: The mean of difference between the model and the experimental data, will

be less to 10 units, with a confidence of 95%.

Situation 3: Assessment of parameters’ relevance on the PMD using a sensitivity

analysis to identify and rank variables as a function of their effects on the model output.

Hypothesis 3: In the phytoremediation process at least one parameter exists that

can be considered as a constringent factor for phytoremediation techniques.

Ho: There is no significant difference between the result mean for the different

parameter variation, with a confidence of 95%.

Ha: There is a significant difference between the result mean for the different

parameter variation, with a confidence of 95%.

1.3. Rationale

The biggest concerns about phytoremediation are soil-plant interactions as a

function of the different contaminants, particularly heavy metals (HM) (Pezzarossa et al.

14

2011). Determining values to characterize the process which governs plant-soil-

pollutant interaction by the phytoremediation dynamic model (PDM) will enhance the

understanding in the field. The model should be used as a teaching-learning tool for

regulatory entities, to explain the system’s behavior; also it will fill the gaps in the

decision making process, evaluating different possible settings, including plant species

(Ackerman et al. 2008).

The development of a dynamic model for the phytoremediation process is

needed to estimate the extraction and flow rate, and the contaminant concentration

(mercury) in plant tissues. Those parameters will provide the opportunity to improve the

viability analysis for phytoextraction. Incorporating this knowledge, a more accurate time

prediction will be achieved for the cleanup level. Also, the analysis can include

economic feasibility to recycle HM and its potential market revenue. The model will

serve as an assessment and teaching-learning tool to understand the system responses

as function of contaminant concentration. Also it will help to determine the economic

benefits of phytoremediation in comparison with traditional HM cleanup techniques. The

EPA has concerns with regard to the best plant species for a particular metal, the time

required to achieve the cleanup, and if the HM collected will be enough to obtain

revenues after recycling them (EPA 2000; Chaney et al. 2007). EPA’s apprehension is

to conduct pilot studies, which are site specific, expensive and time consuming. To

solve the status quo, other approaches like the typical implementation of regression

models to estimate the concentration of HM as a function of plant tissue dry weight, or

mathematical modeling approach using differential equations to estimate the harvest

time and the cleanup compliance levels should to be considered. According to this

scenario, with this dissertation, I developed a comprehensive phytoremediation dynamic

15

model (PDM) to address EPA’s concerns by integrating the system dynamic approach

and the physiological model of the plant.

1.4. Intellectual Merit

The PDM estimates the contaminant (mercury) extraction rate and concentration

in the plant tissues. Those parameters provide the opportunity to improve the viability

analysis for phytoremediation cleanup technique, the time required to achieve the

cleanup level, the feasibility to recycle contaminant (mercury) from the plant tissue, and

its potential market revenue. Also, PDM can be implemented as assessment and

teaching-learning tool to understand system responses, including contaminant

concentration and plant species selection, which will help in the decision making

process.

1.5. Broader Impacts

The construction of a model of the phytoremediation dynamic process will help to

better understand the process inside plants and its interactions. This knowledge can be

used to understand and determine the most relevant parameters of the cleanup process.

The PDM should be used as a teaching-learning tool, to explain the systems’ behaviors

to the regulatory entities and the community, to leverage all group participation.

PDM works with a system dynamic approach based on mathematical

background implementing fluid dynamic differential equations system, on modular

schematic representation which facilitates the inclusion of different synergistic parameter

(variables). Figure 1.05 shows a basics schematic representation of the

phytoremediation process. The representation is composed of four structural blocks and

three processes. Each block is to mimic the contaminant concentration as a function of

plant physiological section (root, shoot, leaf) and soil interaction. The three arrow steps

16

are to exemplify the contaminant flow between blocks. Extraction is to represents the

root capability to extract the contaminant from soil. Translocation is the term typically

used for the contaminant movement form root to plant upper tissue (Lasat 2000). In

order to have a clear distinction, this process has been divided in two steps:

translocation 1 represents the contaminant flow from root to shoot (stem) and

translocation 2 characterizes the contaminant flow from shoot to leaf.

Figure 1.05: Basic schematic

representation of plant physiology,

which represents the phytoremediation

process.

17

Chapter Two

Literature Review

Phytoremediation has not been commercially implemented because of the

existence of several gaps of knowledge regarding its performance, such as extraction

processes (plant physiology) as function of contaminant and how the changes on

environmental factors such as temperature, light and humidity will affect. For instance,

soil properties are crucial to assess the viability of this technique, because they affect

the contaminant mobility and rhizosphere interaction (FRTRa 2006). The soil property

which affects different factors is pH. The pH modifies the interaction between the

contaminant and root, changing the extraction rates. As example, on metal

contamination in acidic soil the desorption process is stimulated (Lasat 2000). Once the

soil physical-chemistry has been characterized according to contaminant targets, a

preliminary group of plants is chosen then, the physiological concerns taken into account

place, mainly toxicity resistance and root characteristic. These factors are crucial to

determine the viability of the approach (EPA 2000, Pezzarossa et al. 2011). Federal

agencies, like the Department of Defense, have several concerns about the

phytoremediation methodology, specifically about the following limitations as discussed

by FRTRb (2006):

(1) The depth of the treatment zone is determined by the plant species used in

phytoremediation. In most cases, it is limited to shallow soils.

(2) High concentrations of hazardous materials can be toxic to plants.

(3) It involves the same mass transfer limitations as other biotreatments.

(4) It may be seasonal, depending on location.

(5) It can transfer contamination across media, e.g., from soil to air.

(6) It is not effective for strongly sorbed and weakly sorbed contaminants.

18

(7) The toxicity and bioavailability of biodegradation products is not always known.

(8) Products may be mobilized into ground water or bioaccumulated in animals.

(9) It is still in the demonstration stage.

(10) It is unfamiliar to regulators.

These concerns can be tackled by developing and implementing mathematical

models to evaluate different systems to make objective decisions without affecting the

environment. These approaches bypass the human rationality that, in some cases,

promotes a systematic error and/or biases (Sterman 1989). The objectivity takes more

relevance in a complex system like the environment, which is constituted by different

structures such as, stocks (level variables), flows (rates) and feedback loops

(interactions). The synergy of these structures produces extensive behavior spectra

defined by time delays, linear and nonlinear interactions. Some examples that

characterized these behaviors are: the predator-prey oscillations, or air pollutions and

matter cycling (Ford 1999; Deaton and Winebrake 2000). Human estimation capability

is unsuccessful to explain this kind of system behaviors, thus, the implementation of

scientific models as tools in the decision-making process will provide the knowledge to

incorporate a comprehensive approach on different strategies and policies (Sterman

1989). Modeling allows to analyze different scenarios, and to determine and ponder the

most relevant criteria to assess system performance (Fisher 2007), features highly

desirable for the environmental decision making process.

The system dynamic approach (SDA) was developed to analyze and describe

the evolution of a system as function of time, implementing the differential equations

which are described in the dynamic flow theory associated stock level, valves and flow

interactions (Ford 1999; Deaton and Winebrake 2000; Fisher 2007). The discovery of

sub-structures to represent the behavior of different systems (archetypes) increased the

19

applications’ portability between disciplines and systems (Senge 1990). SDA was

developed to examine the evolution and effects of system variables on the engineering

fields, but it gradually was applied to population dynamics, sub-system interactions and

other disciplines (Bedeian 2000). This modeling approach has been employed to

analyze the Earth energy system, human population trends, predator-prey interactions,

the tragedy of the commons (Hannon and Ruth 2001), and in successful marine reserve

management (Blad et al. 2006). The scientific community took advantage of this

approach to mimic their systems and sub systems as modules to analyze their

interaction with its constitutive (Costanza and Voinov et al. 2004) to improve the

management decision making process (Ackerman et al. 2008). Different software

packages have been developed to handle the SDA. They can be classified as

expression-base or flow-base approach. The flow-base packages have been proven to

provide a better conceptual understanding of the system modeled. On this classification,

the STELLA (Strongly Typed Lisp Like Language, a system thinking software from Isee

Systems) package is a program with a validated outstanding performance (Rizzo et al.

2006).

Several mathematical approaches have been implemented to understand the

soil-plant interaction during the last forty years (Benbi and Nieder 2003) and for

modeling the phytoremediation cleanup route. Extraction has been identified as the

leading step for the remediation of heavy metal contamination. The research community

has been developing different approaches to understand the extraction process as

function of the soil components and its properties (Zeng et al. 2011). Diverse

mathematical algorithms have been applied to strengthen the phytoremediation model.

Besides the SDA, it has been found that the theoretical point of view provides the

differential equation solution set, defined by models for compartmentalization of the plant

20

physiology, application of variety diffusion laws implementation and statistical

correlations, aimed to understand the phytoremediation phenomena in a comprehensive

way (McCutcheon and Schoor 2003; Robinson et al. 2003; Trapp 2004; Thomas et al.

2005; Japenga et al. 2007; Qu et al. 2010). These models are mathematically intensive

and very specialized. To solve that situation and to enhance system dynamical model a

SDA using STELLA (system thinking software of isee systems) has been implemented

(Ouyang 2002; Ouyang et al. 2007; Ouyang 2008). These implementations have

considered the internal interactions of the contaminant according to the plants’

metabolism. However, these SDA add an excessive complexity to the model, given the

number of parameters considered, ranging from 30 to 43 variables per model (Ouyang

2002; Ouyang et al. 2007; Ouyang 2008). Figure 2.01, depict the schematic

representation of the UTCSP model (Ouyan 2008), showing its complexity. Those

variables are categorized as, calibrated, estimated and assumed. Calibrated variables

are the quantities that will be changed by the modeler to mimic the natural phenomena,

while an estimated and assumed variable implies educated guesses. The categorization

of the variables fluctuated between 9% and 33% for each model which, represents that

each model is a specific for this scenario (Ouyang 2002; Ouyang et al. 2007; Ouyang

2008). These amounts of variables and their differences in the categorization enhance

the model’s complexity, increasing the gap of knowledge and the possibility of

misunderstanding on interdisciplinary work groups. Perhaps, all these initiatives provide

the framework for a unifying system dynamic model. The phytoremediation dynamic

model (PDM) implements the system dynamic modeling philosophy on the classical

plant physiology structure, providing an understandable and comprehensive tool.

21

Figure 2.01: The dynamic model for uptake and translocation of

contaminant from soil-plant ecosystems (UTCSP) constructed using

STELLA (Ouyan 2008).

22

Chapter Three

Methods

3.1. Theoretical Background

The system dynamic approach considers all dynamic processes as collections of

stocks (level variables) and flows (rates). The flux is the dynamic quantity on the model

that does not necessarily have to be a fluid (Hannon and Ruth 2001). The interactions

between model components are governed by differential equations systems. This

approach provides different software applications to schematically represent the same

system in terms of its basic components: stokes, flows, converters and connectors.

Stocks’ are the level variables which can be conceptualized as containers that can store

the flux represented in the model; their level fluctuates as a function of inflow and

outflow. The fluxes are the rate which provides the dynamic quantity (because their

units are quantity/time) in the model. Converters are auxiliary variables used to make

explicit the implementation of certain conditions in the model scheme. Connectors’ are

the schematic representation which symbolizes the relationship between the other

structures (Hannon and Ruth 2001).

STELLATM (system thinking software of Isee Systems) is dynamic software that

implements the pictographic modeling representation, based upon four basic

components: stocks, flows, connectors and converters (Ouyang 2002, 2008). The basic

model features a bathtub, as illustrated in Figure 3.01 for three scenarios: (a) physical

situation to be modeled, (b) pictographic model representation and, (c) the differential

equations. The representation of a system in term of stocks (level variables) and flows

(rates), in a system dynamic modeling approach knows as the Forrester Diagram, in

23

Stoke

InFlow OutFlow

ConnectorConnector

Stoke

InFlow OutFlow

ConnectorConnector

Stock(t) = Stock(t - dt) + (InFlow - OutFlow) * dt

Model

Environment

A

C

B

Figure 3.01: Distinction between the environment and the system to be

modeled; using a bathtub classical example on system dynamic approach

representing the three scenarios: (A) physical situation to be modeled, (B)

pictographic model representation and (C) the mathematical representation

in terms of differential equations (Adapted from Medin and Mota 2006 ).

honor of Jay W. Forrester founder of system dynamic approach (SDA) (Hannon and

Ruth 2001; Medin and Mota 2006). This schematic representation makes dynamic

the system modeling possible, without mathematical literacy barriers that could occur

with the exclusive differential equation description. For those reasons, an SDA is ideal

24

to model ecosystems and building the consensus on environmental management

(Costanza and Ruth 1998; Costanza and Voinov et al. 2004).

Plants, as a living system, have a complex interaction between their parts and

the environment. This complexity is characterized by physiological processes,

phenotypic plasticity, modular architecture and the ability to adapt to environmental

heterogeneity (Qu et al. 2010). The correlation between the system dynamic approach

and the physiological model of plant structure are ideal matches to construct the

Phytoremediation Dynamic Model (PDM).

3.2. Research Methodology

The development of the Phytoremediation Dynamic Model was performed in two

sections: situations and tasks. Each situation was aligned to the established hypothesis,

while tasks were the intermediate steps to achieve the following scenarios:

Situation 1: Development of the schematic representation of the phytoremediation

dynamic transport process for mercury remediation.

Task 1: Reviewing of the technical literature about phytoremediation dynamics.

Task 2: Evaluating the effects of the interactions parameters for their probable

inclusion in the model.

Task 3: Verifying if some fundamental assumptions can be stated, based on the

experimental data.

Situation 2: Validation of the phytoremediation dynamic transport process for mercury

remediation.

Task 1: Running the model with the input of experimental peer-reviewed data for

mercury phytoremediation systems.

25

Task 2: Managing the different model parameters to mimic the corresponding

output.

Task 3: Performing a mean difference statistical test to prove the similarity

between the model and the experimental data.

Situation 3: Assessment of the parameter relevance in the phytoremediation dynamic

transport process for mercury remediation.

Task 1: Selecting a base modeling scenario.

Task 2: Executing the model by changing each parameter to perform a sensitivity

analysis varying them with ¼, ½, 1, 2 and 4 multipliers.

Task 3: Performing a mean difference statistical analysis to test the result’s

dependence for each parameter and its magnitude.

All graphical and statistical analysis was performed using Minitab 16TM. To

analyze Situation 1 a graph on a daily scale was constructed to compare the

experimental data and the results of PDM. Once the base scenario was selected, a

descriptive analysis was implemented to assess the order of magnitude difference for

the mean, range and standard deviation values, and a regression fit to evaluate the

relationship between data sets. Due to the absence of randomness in the systems,

Situations 2 and 3 were examined with two non-parametric statistics: the Sign Test and

Kruskal-Wallis, to determine the difference between data sets and significance of the

sensitivity analysis, respectively.

For the Phytoremediation Dynamic Model (PDM) five stocks and their

interactions were analyzed. The plant was represented by three functional parts (root,

shoot, leaf) as stocks (level variables) interconnected, mimicking its’ anatomy and

physiology; two stocks represented abiotic factors (soil, atmosphere) of the environment.

26

Figure 3.02: Sequences of tobacco plant compartment based on plant

anatomy and physiology implemented in the Plant Kinetic Model

(Sundberg et al. 2003).

This procedure was selected because it is well known and validated within the scientific

community (Sundberg et al. 2003; Thomas et al. 2005; Ouyang 2008).

The PDM for heavy metal cleanup was developed using an adaptation of the

mathematical models presented by Sundberg et al. (2003) and Thomas et al. (2005),

while the segmentation of the plant physiological parts responds to the protocol

described by Ouyang et al. (2007) and Ouyang (2002, 2008). Sundberg et al. (2003) on

the Plant Kinetic Model (PKM), established a set of five compartments, four of them to

mimic tobacco plant anatomy and physiology, as depicted in Figure 3.02.

The flow interaction on the PKM is governed by gradient difference between the

compartments. For the present PMD study, this assumption was followed, but included

a threshold contaminant level to activate the flow rates between compartments. The

27

PDM incorporated the pollutant saturation point and constant transfer rate, assumption,

as recommended by Thomas et al. (2005), who designed a pure differential equation

model considering assumptions related to pollutants saturation point, constant transfer

rate, immediate transfer rate and bi-flux of pollutant.

For modeling the phytoremediation process, three pairs of compartments

representing the xylem and phloem in the root, stem and leaf, were considered to

simulate the contaminant exchange between compartments, following the procedures

described by Ouyang et al. (2007) and Ouyang (2002, 2008). To avoid these

complexities, PDM considered only the upward net flux of the pollutant, through the plant

model structure to avoid conceptual, mathematical and validation complexities. The

here in PDM modeled considered two main interactions: underground (rhizosphere, soil-

plant) and above ground (contaminant’s dynamic to the atmosphere) soil-plant-

atmosphere interactions as represented in Figure 3.03.

3.3. Model Construction and Validation

The backbone of the Phytoremediation Dynamic Model (PDM) was the

schematic representation shown on Figure 1.05, which followed different modeling

approaches and assumptions about the functional structure of plant physiology, as

discussed by Stern et al. (2003), Sundberg et al. (2003), Thomas et al. (2005), and

Ouyang (2007, 2008). To simplify the dynamic between xylem and phloem, the PDM

considered the upward net flow between the physiological structure representations.

The contaminant flow rate on each section of the model was dependent on the

concentration difference between the plant structural representations. This assumption

has been taken to harmonize the model with the scientific literature, which established

an average contaminant concentration on each physiological part on the plant (Yu et al.

28

2001, Hussein et al. 2007), and consistent with the Plant Kinetic Model (Sundberg et al.

2003).

Figure 3.03: Dynamic structure diagram for the Phytoremediation

Dynamic Model (PDM), in which the system has been divided in the

compartments to be considered. The compartments can be classified

as above or below the ground. The (A) compartment represents the

soil-plant interaction at the root zone, which is the below the ground

section involving two stocks: soil and root. The above ground

segment; are composed by three stocks: (B) shoots, (C) leaf, and (D)

atmosphere.

A

B

C

D

29

In order extend the applicability of the modeling approach another stock was

been added to represent the contaminant concentration released to the atmosphere.

After the incorporation of the assumptions described in the research literature and the

application of STELLATM (Strongly Typed Lips Like Language; system thinking software

of Isee Systems), the schematic representation of PDM was developed. It was

composed by five stocks, four flows and eight auxiliary variables as depicted in Figure

3.04. Stocks (levels variables) represented structural reservoirs of the plant physiology

and environment, while flows (rates) characterized the upward net contaminant

exchange between its compartments. In the literature do not make a distinction between

the flows that supplied substance to shoot or leaf, both of them was called translocation

as shown in Figure 1.05 (Lasat 2000). To avoid misunderstanding on PMD

translocation-2 was renamed as incorporation, which is the flow that supplies the

substance to the leaf. The auxiliary variables are the parameters which govern the

model behaviors categorized as: assumed, estimated and calibrated (Ouyang 2007;

Ouyang 2008). Also, Figure 3.04 shows the differential equation system, which governs

the model behavior. The mathematical expressions depicted in Table 3.01 were

obtained after rewriting according to the standard of mathematical notations.

As shown in Table 3.01, the S_ function represented stocks, with their respective

sub-index (Soil, Root, Shoot, Leaf or Atm for atmosphere). The expression Init_SSoil,

corresponded to the initial contaminant concentration in the soil, which is implemented

as a constant to calculate the bioavailability as time evolves. The ThC_ identified the

threshold contaminant concentration to initiate the movement through the system; R_

meant the rates at which the contaminant move ones the threshold was attained. The

function of the threshold, flux rates and the gradient in concentration between their

30

Figure 3.04: (A) The Forrester Diagram schematic representation of the

Phytoremediation Dynamic Model. (B) The differential equation system of the

phytoremediation process.

neighbors’ stocks was represented by F_. Each one of these functions has a sub-index

which identifies the interaction in the model (Ext = Extraction, Tran = Translocation, Inc =

Incorporation, Vol = Volatilization).

31

Table 3.01: Differential equation system which describes PDM.

Model section Model structure Mathematical representation

Soil

Stock ExtSoil Fdt

dS

Flow Ext

Fraction

Soil

SoilSoilExt R

SInit

SSF *

_*

Root

Stock TranExtRoot FFdt

dS

Flow TranRootRootTran RThCSF *

Shoot

Stock IncTranShoot FFdt

dS

Flow IncShootShootInc RThCSF *

Leaf

Stock VolInc

LeafFF

dt

dS

Flow VolLeafLeafVol RThCSF *

Atmosphere Stock VolAtm Fdt

dS

32

Once the PDM was schematically and mathematically described (Figure 3.04,

Table 3.01), the fundamental assumptions which govern the model behaviors with their

corresponding scientific background were stated as follow:

1. Fluxes (rates) depend of the contaminant concentration of the previous

stocks (level variables), its rates and threshold concentration on previous

stocks. The threshold concentration established the minimum

concentration on the previous stock, which allows the contaminant flow to

the next stocks. Threshold concentration are constant during the time

frame modeled (Root threshold concentration, Shoot threshold

concentration, Leaf threshold concentration). This works as osmotic

concentration levels, which is a phenomenon observed as a function of

plant species and contamination, as reported for plant tissues (Yu et al.

2001; Jadia and Fulekar 2009; Sarma 2010).

2. Flux rates was constant during the time frame modeled (Extraction rate,

Translocation rate, Incorporation rate, Volatilization rate). In plant

physiology it is well known that ions in solution are moved through

transporters. These transporters are characterized mainly by their transport

capacity (Vmax) and affinity for the ion (Km) (Lasat 2000). Once the

threshold concentration was achieved the flow was constant around plant

transport capacity.

3. Initial level concentrations in different stocks are zero, except for the stock

which represents contaminated soil.

4. Contaminant bioavailability depends on the exponential ratio between the

current and initial contaminant concentration in soil. This dependence was

33

represented in the flow equation in PMD soil section and was called

Fraction. This soil-plant includes factors such as plant transporters and soil

physic-chemical properties. The Km measures the transporter affinity for a

specific ion, where high values represent low affinity. The contaminant

bioavailability has complex interactions with soil pH, organic matter,

carbonates, electrical conductivity and grain distribution (Benbi and Neider

2003). The pH is one of the most important chemical properties of the soil

because affects the bioavailability of the contaminant, through the

modification of the cation exchange capacity (Lasat 2000). The heavy

metal concentration as a function of pH, has a strong correlation coefficient

on a logarithmic lineal regression (Almendras et al. 2009, Rodríguez et al.

2009, Zhang et al. 2010).

The PDM has been developed to mimic phytovolatilization, phytoextraction and

rhizofiltration processes. The most challenging process to model is phytovolatilization

because it includes all physiologic processes in the plant. Phytovolatilization processes

was selected to validated PDM, according to peer review experimental data but, heavy

metal accumulation and hyperaccumulation plant have been studied extensively (Sahfiul

et al. 2010; Sarma 2011), only a few research has been performed on heavy metal

phytovolatilization (Rugh et al. 1996; Bizily et al. 1999, 2000; Ruiz et al. 2003; Hussein et

al. 2007). The accumulation concentration values in plant tissues were used to compare

and establish the threshold values for each physiological structure (Rugh et al. 1996;

Bizily et al. 1999, 2000; Ruiz et al. 2003). Hussein et al. (2007) showed a

comprehensive process of phytovolatilization of heavy metal as time evolves. They

reported two types of mercury (mercury chloride (HgCl2) and phenyl mercury acetate)

34

phytovolatilization experimental data for two genetically modified and wild type tobacco

plants. Also, they quantified the mercury concentration on the physiological section of

the plant and mercury concentration volatilized as time evolves on a day based scale.

The system characterization as time evolves is an essential requirement for a system

dynamic approach. The validation was performed using HgCl2 and the pLDR-merAB

transgenic line of tobacco plant. The selection was made because all of the

fundamental assumptions of the PDM were established about the plant used the ion

transporter to clean up contaminated soil, which has been hypothesized as crucial

mechanism (Lasat 2000).

The volatilization data for the two genetically modified lines are shown in Figure

3.05. Using the root and shoot mercury concentration data values, the auxiliary variable

was estimated to establish the threshold data value to activate the upward net flow

between the physiological plant section. Estimated parameters are auxiliary variables

which values were extracted or approximated from experimental data, and then fixed.

Calibrated parameters are auxiliary variables identified as behavioral control variables.

The application of the above definitions together with the Hussein et al. (2007) data,

allowed the PDM auxiliary variables categorization and the determination of their values,

as shown in Table 3.02.

The data set of the transgenic line pLDR-merAB were employed for validation

purposes, because they represent the simpler gene expression and present more

behavioral changes in comparison with pLDR-merAB3’UTR (Figure 3.05). For validation

proposes a Systematic Dynamic Approach (SDA) model (Figure 3.06) was constructed

to obtain the cumulative volatilized mercury using the experimental volatilization rate.

This transformation provided another time dependent experimental data set, which

35

provided two different data matrix for validation purposes. Both data sets were

implemented to qualitatively validate the PDM (Figure 3.07), allowing the concurrence

between the model and the experimental values.

Figure 3.05: Volatilization data by genetically modified tobacco plant on

contaminated soil with 100 µM of HgCl2 (Adapted from Hussein et al.

2007).

14121086420

4

3

2

1

0

Days

µg

Hg

vo

lata

lize

d (

1/

g) d

ry

we

igh

t (

1/

d)

pLDR-merAB

pLDR-merAB3UTR

Variable

36

Table 3.02: Auxiliary variable categorization and base scenario values.

Name Category Base scenario value

Root threshold Estimated 500 µg Hg

Shoot threshold Estimated 4 µg Hg

Leaf threshold Estimated 1 µg Hg

Volatilization rate Estimated 1 µg Hg/(d* µg Hg in leaf)

Extraction rate Calibrated 0.1315 µg Hg/(d* µg Hg in soil)

Translocation rate Calibrated 0.0725 µg Hg/(d* µg Hg in root)

Incorporation rate Calibrated 0.3550 µg Hg/(d* µg Hg in shoot)

Fraction Calibrated 70

Figure 3.06: Schematic representation of stock (level

variables) and flow model to obtain the cumulative

volatilized mercury, using experimental data.

37

Figure 3.07: Comparison between experimental data and PDM. (A)

Volatilized µg Hg. (B) Cumulative volatilized µg Hg.

14121086420

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Days

Hg

vo

lati

liza

ed

(µ

g)

Model

Experimental

Variable

A

14121086420

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Days

Hg

cu

mu

lati

ve

vo

lati

liza

ed

(µ

g)

Model

Expeimental

Variable B

38

Chapter Four

Results

4.1. General Findings

The capability of PDM to mimic a phytoremediation (phytovolatilization) system

on a qualitative way was demonstrated (Figure 3.07). The followings were also

validated: 1) the fundamental assumptions of the model and 2) the value of the auxiliary

variable in the base scenario which are reasonable and feasible (Table 3.02). The

model has eight auxiliary variables that have been categorized; four as estimated and

four as calibrated. The categorization was performed according to the way in which their

value was obtained, estimated for the value extracted from the literature and calibrated

for the variables values modified to adjust model behaviors to the experimental data.

Those variables are also divided in three groups: threshold, rates and bioavailability.

Threshold values are the minimum contaminant concentration that the physiological

plant sections should have to initiate the flow rate to the next physiological plant section.

Rates values are the contaminant flow between two consecutive physiological plant

sections by days after the threshold values was achieved. Bioavailability group is

composed only by the Fraction variable, which characterized the percentage of

contaminant that is not available for extraction.

The bioavailability term of the contaminant in the model was constructed as

exponential dependence of the ratio of contaminant concentration in soil divided by the

initial contaminant concentration in soil, having as exponent the Fraction auxiliary

variable. This construction has been driven by the bioavailability contaminant

dependence of soil physical and chemical factors, such as: pH, organic matter,

39

carbonates, electrical conductivity and grain distribution. This term put together all of

this dependence summarized in the Fraction auxiliary variable.

Table 3.02, depicts the variable base scenario values which PDM mimics the

experimental phytovolatilization data as shown in Figure 3.07. The ranking in ascending

order of the threshold according to the contaminant concentration is: leaf < shoot (4

times leaf) < root (125 times shoot). The position in ascending order of the rates is:

translocation (around 0.55 times extraction) < extraction < incorporation (around 2.70

times extraction). The volatilization rate was excluded because it was estimated instead

of calibrated as the other rates and the estimation means that the same amount of

contaminant achieve the leaf is volatilized (1 µg Hg/(d* µg Hg in leaf). The root has the

higher value according to the threshold but its corresponding rate (extraction) obtained

the middle value. This magnitude relationship needs to be carefully analyzed because; it

can be a determining factor to the phytoremediation process.

Graphical model behaviors for both variables (mercury volatilization rate and

cumulative mercury concentration) have been shown in Figure 3.07, the specific data

sets related with those graphs are shown in Table 4.01. During the first two days both

data sets has as a value of zero, even in the model or experimental data. This is a

typical performance in phytoremediation data sets. An important characteristic

according to the data behavioral analysis is that the data values for mercury volatilization

rate and cumulative mercury concentration have the same order of magnitude. Using as

benchmark the cumulative mercury concentration, it can be observed after day seven

that the data (model or experimental) achieved a steady state, which can be related with

the tissue saturation point. Table 4.02 depicts the basic statistical by approach for each

data variable. The means by variable for each approach showed a difference of a power

40

Table 4.01: Data sets used for graphical validation.

Volatilized rate of Hg (µg Hg) Cumulative volatilized (µg Hg)

Days Experiment Model Experiment Model

0 0.00 0.00 0.00 0.00

1 0.00 0.00 0.00 0.00

2 0.00 0.00 0.00 0.00

3 0.38 0.42 0.38 0.42

4 1.63 1.19 2.00 1.61

5 0.63 0.92 2.63 2.53

6 0.38 0.62 3.00 3.15

7 0.38 0.25 3.38 3.40

8 0.00 0.00 3.38 3.40

9 0.00 0.00 3.38 3.40

10 0.00 0.00 3.38 3.40

11 0.00 0.00 3.38 3.40

12 0.00 0.00 3.38 3.40

13 0.00 0.00 3.38 3.40

Table 4.02 depicts the basic statistical by approach for each data variable. The

means by variable for each approach showed a difference of a power of 10 between

cumulative and volatilized data values for each approach (experimental or model). Also,

the range difference between variable was twice for the experimental approach and

three times for the model approach. The standard deviation (s) of the cumulative data

set was more than three times greater than the volatilized value. The coefficient of

41

variation (CV) shows an inverse relationship in comparison to the standard deviation; the

volatilized data is more than twice of the cumulative ones.

The coefficients of variation are comprehensive parameters which include

standard deviation and mean (Daniel 2009). In both approaches the results

demonstrated that the cumulative data sets were the most consistent in comparison with

the volatilized data. Also, the total cumulative mercury concentration is the most

relevant value environmentally speaking because, those emission enhance the mercury

concentration in the atmosphere.

Due the absence of randomness of the data and to fulfill the research objectives,

different non-parametric statistical analyses were executed and their outcome described

as follow.

Table 4.02: Descriptive statistical analysis for volatilized and cumulative mercury

concentration (µHg) by approach. (standard deviation (s); coefficient of variation (CV))

Approach Variable N Mean Range s CV

Experimental

Cumulative 14 2.262 3.380 1.477 65.33

Volatilized 14 0.243 1.630 0.4524 186.3

Model

Cumulative 14 2.251 3.400 1.496 66.47

Volatilized 14 0.243 1.190 0.398 163.92

42

4.2. Quantitative Equivalence between PDM and Experimental Data

To evaluate the relationship between the experimental data and PDM data output

a regression fit analysis has been performed, as shown in Figure 4.01. The analysis

demonstrated a strong correlation (99.4%) between the model and the experimental

data. The slope of the regression line differed less than 0.1 percentages in comparison

with the theoretical one. All data points achieved the 99% prediction interval; however

one data point (7%) was tangential with the line that constringes the interval. The

prediction interval represents a range that a single new observation is likely to fall,

according to the established percentage of precision. The 86% of data points are inside

the confidence interval, one (7%) is touching the lines that limit the interval and another

(7%) is completely outside the interval. The confidence interval represents a range that

the mean response, according to the established percentage of precision. Those

statistical results demonstrated that Phytoremediation Dynamic Model (PDM) have the

capability to reproduce the experimental result of phytoremediation experiment with

excellent degree of certainty.

The descriptive statistical analysis shown in Table 4.02 reveals that the data

values are narrow (range are the same magnitude order of the mean). In the Figure

4.02, can be observed the box plot distribution of the experiment, model and their

difference (model minus experimental). The box plot of the model and experimental

data are very similar in range but, have different space distribution in the interquartile

range (Q1-Q2 and Q2-Q3). In the box plot of the difference, the median is close to zero

and has three outlier values which are identified by an asterisk (*). With the information

depicted in Figures 4.02, it can be argued that the difference the between model and the

experimental data is less than 10 data units; more than that can be enunciated that the

43

difference is less than 1 data units. The graphical representation is not enough to claim

the significance of the difference, a statistical test is needed.

Figure 4.01: Regression fit analysis between experimental data and PDM,

showing the prediction (PI) and confidence (CI) intervals for cumulative

mercury concentration.

3.53.02.52.01.51.00.50.0

4

3

2

1

0

Experimental

Mo

de

l

R-Sq 99.4%

R-Sq(adj) 99.3%

Regression

99% CI

99% PI

Model = - 0.03193 + 1.009 Experimental

44

Figure 4.02: Box plot comparison of experimental data, model and the

difference between experimental data and the model for cumulative

mercury concentration. The outliers are represented by asterisk (*).

DifferenceModelExperimental

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Hg

cu

mu

lati

ve

vo

lati

liza

ed

(µ

g)

As the data does not satisfy the requirement for a parametric test, thus a Sign

Test was employed as a non-parametric statistic to examine the mean difference

between the model and the experimental data for cumulative volatilized mercury

presented in Table 4.01. Table 4.03 shows the results of the Sign Test, which

demonstrated that the null hypothesis can be rejected with a significant confidence level

of 95%, having median of 0.0200 and a p-value of 0.0001. Figure 4.03 illustrates that a

difference within the 95% of confidence level, between experimental data and the model

cannot be appreciated. This validates statistically that PDM does not have a mean

difference greater or equal to 10 measurements, in comparison with experimental data.

45

4.3. Sensitivity Analysis for the Calibrated Variables

To assess the constringent factor of phytoremediation process a sensitivity

analysis was performed classified as calibrated. The variables the Table 3.02 (refer to

page 39), were included in the sensitivity analysis. Each has a base scenario value

depicted in Table 3.02. To analyze the effect of the variable according to the response

(cumulative volatilized mercury), the base scenario was multiplied by ¼, ½, 2 and 4. In

this analysis the data point after the third day was considered because the response

studied (cumulative volatilized mercury) began to have value at this time. The first

sensitivity analysis was the average response by variable (Figure 4.04). According to

the average response for the fourth treatment by day, the variables can be ranked in

ascending order as: incorporation < translocation < fraction < extraction. This shows

that the most sensitive variables are the ones which relate to the first step in the model.

Those variables (fraction and extraction) are responsible for the interaction between the

biotic (roots) and abiotic (contaminated media) section. To obtain a specific behavior

analysis a sensitivity test by variables was been performed for the different scenarios.

The fraction variable describes the percentage of the contaminant attached to the soil

(no bio-disposable), in this analysis it shows an inverse relationship (Figure 4.05).

Table 4.03: Sign test for a confidence level of 95%, testing: median = 10.00

versus median ≠ 10.00

N Below Equal Above P Median

Difference 14 14 0 0 0.0001 0.0200

46

Figure 4.03: Comparison of mean and confidence interval between the

experimental data and PDM.

ModelExperimental

3.5

3.0

2.5

2.0

1.5

1.0

Hg

Cu

mu

lati

ve

vo

lati

lize

d (

µg

)

95% CI for the Mean

As the fraction variable is bigger the contaminant has less mobility. In Figures

4.06, 07 and 08 it can be observed that all calibration variables responded directly

according to the treatment and similar to quadratic relationships according to the mean

by treatment. The separate graphical analysis is consistent with the previous positioning

order according to the average response and also shows that the response range varied

by power of ten, between consecutive variables.

47

Figure 4.04: Sensitivity analysis of average cumulative

volatilized mercury by variables.

1412108642

500

400

300

200

100

0

Days

Av

era

ng

e c

um

ula

tiv

e v

ola

tili

ze

d m

erc

ury (

µg

)

Fraction

Extraction

Translocation

Incorporation

Variable

48

1412108642

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150

100

50

0

Days

Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

1412108642

200

150

100

50

0

Days

Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

1

2

Figure 4.05: Response as function of fraction, according to the different

scenario: (1) sensitivity analysis of average cumulative volatilized

mercury, (2) Mean confidence interval of 95% of the absolute value of

the difference between the response and base scenario.

Fraction (D)Fraction (C)Fraction (B)Fraction (A)

180

160

140

120

100

80

60

40

20

0

Ab

s(R

esp

on

se

-B

ase

)

1412108642

200

150

100

50

0

Days

Av

era

ng

e c

um

ula

tiv

e v

ola

tiliz

ed

me

rcu

ry

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

49

Extraction (D)Extraction (C)Extraction (B)Extraction (A)

1800

1600

1400

1200

1000

800

600

400

200

0

Ab

s(R

esp

on

se

-Ba

se

)

1412108642

1600

1400

1200

1000

800

600

400

200

0

Days

Av

era

ng

e c

um

ula

tiv

e v

ola

tiliz

ed

me

rcu

ry

Extraction (A)

Extraction (B)

Extraction (C)

Extraction (D)

Variable

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

Figure 4.06: Response as function of extraction, according to the different

scenario: (1) Sensitivity analysis of average cumulative volatilized

mercury, (2) Mean confidence interval of 95% of the absolute value of the

difference between the response and base scenario.

1

2

1412108642

200

150

100

50

0

Days

Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

1412108642

200

150

100

50

0

Days

Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

50

Figure 4.07: Response as function of translocation, according to the

different scenario: (1) Sensitivity analysis of average cumulative volatilized

mercury, (2) Mean confidence interval of 95% of the absolute value of the

difference between the response and base scenario.

Translocation (D)Translocation (C)Translocation (B)Translocation (A)

25

20

15

10

5

0

Ab

s(R

esp

on

se

-Ba

se

)

1412108642

25

20

15

10

5

0

Days

Av

era

ng

e c

um

ula

tiv

e v

ola

tiliz

ed

me

rcu

ry

Translocation (A)

Translocation (B)

Translocation (C)

Translocation (D)

Variable

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

1

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

2

1412108642

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150

100

50

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Days

Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

1412108642

200

150

100

50

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Days

Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

51

Figure 4.08: Response as function of incorporation, according to the

different scenario: (1) sensitivity analysis of average cumulative volatilized

mercury, (2) mean confidence interval of 95% of the absolute value of the

difference between the response and base scenario.

Incorporation (D)Incorporation (C)Incorporation (B)Incorporation (A)

8

7

6

5

4

3

2

1

0

Ab

s(R

esp

on

se

-Ba

se

)

1412108642

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6

5

4

3

2

1

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Av

era

ng

e c

um

ula

tiv

e v

ola

tiliz

ed

me

rcu

ry

Incorporation (A)

Incorporation (B)

Incorporation (C)

Incorporation (D)

Variable

C = Base scenario * 2

D = Base scenario * 4

A = Base scenario* ¼

B = Base scenario * ½

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

1

2

1412108642

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150

100

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Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

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150

100

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Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

52

4.4. Phytoremediation Constringent Factor Determination

Figure 4.04 of the sensitivity analysis depicts that the extraction variable

response has the most abrupt value change. This finding is confirmed in Figure 4.09,

which shows the mean confidence of absolute value of the differences between the base

scenario and the response by variable and treatment. PDM with this sensitivity analysis

has been shown that contaminant concentration on the upper soil tissue depends

strongly on the extraction rate.

Statistical tests need to be applied to evaluate the significance of this finding.

The non-parametric Kruskal-Wallis test was applied to analyze the statistical difference

between the variables as a function of the treatments; the results are shown in Table

4.04, which demonstrated that there is a significant statistical difference between the

Factor

Treatment

TranslocationIncorporationFractionExtraction

DCBADCBADCBADCBA

1800

1600

1400

1200

1000

800

600

400

200

0

Ab

s(R

esp

on

se

-Ba

se

)

95% CI for the Mean

Figure 4.09: Mean confidence interval of 95% of the absolute value of

the difference between the response and base scenario by factor and

treatment.

A = Base scenario* ¼

B = Base scenario * ½

C = Base scenario * 2

D = Base scenario * 4

1412108642

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150

100

50

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Days

Cu

mu

lativ

e v

ola

tiliz

ed

me

rcu

ry

(µ

g)

Fraction (A)

Fraction (B)

Fraction (C)

Fraction (D)

Variable

53

variables. However, this test does not demonstrate conclusively the existence of the

constringent variable for the phytoremediation cleanup process. If the Z values are used

to rank the response variable dependence, the results are consistent with the findings

depicted in Figure 4.04. In both cases the extraction rate has been ranked as the most

influent variable of the model.

Table 4.04: Krustal-Wallis statistical test of the sensitivity analysis.

Factor N Median Average Rank Z score value

Extraction

Fraction

Incorporation

Translocation

Overall

44

44

44

44

176

3.4150

0.2500

1.6050

3.0500

121.0

75.3

68.0

89.7

88.5

4.89

-1.99

-3.08

0.18

H = 28.06 DF = 3 P = 0.000

H = 28.11 DF = 3 P = 0.000 (adjusted for ties)

To obtain definitive results about the variable which contribute the most on

response change, the Tukey test was employed as shown in Table 4.05. This analysis

confirms the previous results, establishing that extraction is the variable which promotes

the statistical difference on the model response. Also, this finding is endorsed by the

confidence interval illustrated in Table 4.06, in which the extraction interval has a large

difference besides others intervals. Those analyses prove that the extraction rate is the

54

constringent factor on the phytoremediation process according to the Phytoremediation

Dynamic Model (PDM) findings.

Table 4.05: Grouping information using Tukey Method.

Factor N Mean Grouping

Extraction

Fraction

Translocation

Incorporation

Base

44

44

44

44

44

454.8

34.0

8.2

3.8

2.9

A

B

B

B

B

Means that do not share a letter are significantly different.

Table 4.06: Individual 95% confidence interval based on pooled standard deviation (sp).

Factor N Mean s Mean interval based on sp = 273.8

Base

Extraction

Fraction

Incorporation

Translocation

44

44

44

44

44

2.9

454.8

34.0

3.8

8.2

1.0

609.2

60.1

2.4

9.0

(----*----)

(----*-----)

(----*----)

(----*----)

(-----*----)

-----+------------+-------------+-------------+-------

0 160 320 480

55

Chapter Five

Discussion

Phytoremediation has not been commercially implemented because of the lack of

knowledge about the process. The Phytoremediation Dynamic Model (PDM) already

shows its capability to provide useful information to assess the performance of this

approach. For example, the interaction between the contaminant, soil and the

rhizosphere can be summarized and modeled using the PDM variable (Fraction and

Extraction Rate). Those interactions have been already identified as a constringent

factor and lack of knowledge (Lasat 2000; FRTRa 2006). The plant type (species or

clone) selection according to different scenario is also, a big concern (EPA 2000,

Pezzarossa et al. 2011). Most of the concerns are related to answering these questions:

1. How will the plant respond at different contaminant concentrations?

2. Which plant type (species or clone) is the best selection according to the

specific situation?

These questions can be answered by the PDM. These, as a modeling approach,

bypass human rationality that, in some case, promotes a systematic error and/or bias

(Sterman 1989). To illustrate how this is performed with PDM and the model plasticity

the fallowing sections have been developed.

5.1. Concentration Response and Performance

To demonstrate how PDM can be used to determine the contaminant

concentration response and calculate the percentage of the contaminant removed the

mercury phytovolatilization data in Hussein et al. (2007) has been used. Having

validated the PDM with the mercury chloride (HgCl2) and transgenic line pLDR-merAB,

all the variable values have been fixed (Table 3.02), except for Fraction. The Fraction is

the variable which unifies the biotic and abiotic parameters to determine the

56

bioavailability of the contaminant. In order to include the influence of the different

contaminant concentrations into the bioavailability factor, the variable Fraction was

multiplied by the ratio between the concentrations evaluated and validated (100 µM).

Implementing this approach the PDM for a concentration range of 10 µM to 100

µM, with an increment of 10 µM, to assess the contaminant concentration effects in the

phytovolatilization system has been executed. The phytovolatilization process was only

reached for the concentration of 100 µM. This result was obtained because the root

does not accomplished the threshold concentration (500 µg), for the values less than

100 µM (Figure 5.01-A). At the concentration of 95 µM, the system began to volatilize

after 9 days and for 105 µM the volatilization response started after 3 days (Figure 5.01-

B). This result is interesting because, with the implementation of PDM a different

behavior for a phytoremediation system was found. Implicating that this system that has

been genetically design for a phytovolatilization process also can be used as

phytoextraction system in soil contaminated with mercury chloride (HgCl2) in which

concentration is less or equal to 90 µM.

The data of total percentage of contaminant removed can be used as a

performance factor. Figure 5.02 depicts the performance behavior. The total

percentage of mercury removal has an inverse relationship with the amount of the

contaminant in the soil. The percentage of contaminant removal of this

phytoremediation system has a range of 18% (from 31% to 13%). In contaminant soil

concentrations between 10 µM to 40 µM, the greatest dependence in mercury removal

(Figure 5.02-A) can be observed and after 50 µM remain basically constant, around 13%

of mercury removal (Figure 5.02).

57

Figure 5.01: Soil contaminant concentration gradient curve to assess the

effects in the phytovolatilization system. (A) Cumulative mercury in the

root as function of initial soil contaminant concentration. (B) Cumulative

volatilized mercury as function of initial soil contaminant concentration.

14121086420

600

500

400

300

200

100

0

Days

Cu

mu

lati

ve

me

rcu

ry i

n r

oo

t (µ

g)

100µM

10µM

20µM

30µM

40µM

50µM

60µM

70µM

80µM

90µM

Variable

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3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

Days

Cu

mu

lati

ve

vo

lati

lize

d m

erc

ury

(µ

g)

95µM

100µM

105µM

Variable

A

B

58

Close to 100 µM (± 5%) the percentage of total mercury removal varied in the hundredth

(Figure 5.02-B).

Hussein et al. (2007) established that the uptake saturation limit for this

phytovolatilization system with a 200 µM of mercury chloride (HgCl2) in the soil. Their

article does not show the phytovolatilization data for this concentration. However, it has

the amount of mercury by tissue (root and shoot) after 15 days is reported. by applying

PDM for this configuration the system behaviors’ can be analyzed. Figure 5.03 depicts

the phytoremediation system behaviors in term of cumulative volatilized mercury and

total percentage of mercury removal for two different contaminant soil concentrations

(100 µM and 200 µM). After the third day the cumulative volatilized mercury achieve

steady stay (Figure 5.03-A) and for the total percentage of mercury removal remain

constant (Figure 5.03-B), for 200 µM curve.

This analysis performed with PDM the evaluated phytoremediation system

behaviors in function of the soil contaminant concentration increased the information

available at the time to make a decision. It also provides a better understanding of

regulators, of the system’s functionality.

59

Figure 5.02: Percentage of mercury removal as a function of initial soil

contaminant concentration. (A) From 10 µM to 100 µM, with an increment

of 10 µM. (B) 100 µM ± 5%.

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B

60

Figure 5.03: Behavioral analysis as a function of contaminant soil

concentration value of 100 µM and 200 µM. (A) Cumulative volatilized

mercury. (B) Percentage of total mercury removed.

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200µM

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5.2. Plant Type Determination (species or genetically modified)

One of the biggest concerns about phytoremediation approach is plant selection

(EPA 2000, Pezzarossa et al. 2011). Using PDM as a plant performance evaluation

tools for a specific scenario an objective selection will be performed. To demonstrate

this capability the mercury phytovolatilization data in Hussein et al. (2007) has been

used. Having validated the PDM for the transgenic line pLDR-merAB, in this section

evaluated the behavior of pLDR-merAB3’UTR transgenic line, for mercury chloride

(HgCl2) have been. For this validation all estimated variables maintained their previous

value (Table 3.02). The Fraction is kept fixed because it determines the contaminants

bioavailability, in both experiments (pLDR-merAB and pLDR-merAB3’UTR) the same

type of soil condition and contaminant concentration (Hussein et al. 2007) has been

used. Table 5.01 shows the values used for the calibrated variables that reproduce the

experimental data behaviors.

Following the same approach implemented for the line pLDR-merAB, the

cumulative volatilized values for the analysis have been used. A good agreement

between the experimental data and PDM is depicted in Figure 5.04. As discussed

previously a qualitative analysis is no enough to establish the model significance or

Table 5.01: Calibrated auxiliary variable and values, for pLDR-merAB3’UTR transgenic

line.

Name Value

Extraction rate 0.1315 µg Hg/(d* µg Hg in soil)

Translocation rate 0.0725 µg Hg/(d* µg Hg in root)

Incorporation rate 0.3550 1 µg Hg/(d* µg Hg in shoot)

62

correlation between experimental data. To evaluate the statistical significance of this a

Sign test was performed, which demonstrated that the null hypothesis can be rejected

with a significant confidence level of 95%, having median of the difference of 0.0200 and

a p-value of 0.0001 (Table 5.02). This statistically validates that PDM does not have a

mean difference superior or equal to 10 measurements, in comparison with experimental

data. Figure 5.05 illustrated regression fit analysis in which a strong correlation (99.6%)

between PDM and the experimental data, also all data achieve the prediction interval of

99%.

Having showed the agreement between experimental data and the values

obtained by PDM, a comparison between the two transgenic lines (pLDR-merAB and

pLDR-merAB3’UTR) is feasible. According the volatilization data (Figure 3.05) the

transgenic line pLDR-merAB3’UTR have better performance (Hussein et al. 2007).

Analyzing the rates at which the contaminant is flowing through the plant can be

observed that in the only rate pLDR-merAB has greatest value is in the Extraction rate,

having a 2.3% of difference (Table 5.03). This rate has been determined as a

constringent factor for phytoremediation process. The pLDR-merAB3’UTR obtained the

higher values for the Translocation (161.3%) and Incorporation rates (82.2%), as

showed in Table 5.03. Considering those result, the plant biotechnology scientific

community can evaluate the physiological response of the plant according to the specific

gene insertion. In this case the insertion of 3’UTR gene decreased the contaminant

extraction rate capability but, increased the contaminant flow rate inside the plant.

63

Figure 5.04: Comparison between experimental data and PDM, for the

cumulative volatilized mercury by pLDR-merAB3’UTR transgenic line.

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Model

Experimental

Variable

Table 5.02: Sign test for a confidence level of 95%, testing: median = 10.00 versus

median ≠ 10.00

N Below Equal Above P Median

Difference 14 14 0 0 0.0001 0.4750

64

Table 5.03: Calibrated auxiliary variable values in µg Hg/(d* µg Hg in Socks) for pLDR-merAB and pLDR-merAB3’UTR transgenic lines.

Variable pLDR-merAB pLDR-merAB3’UTR Percentage of

difference

Extraction rate 0.1315 0.1285 2.3

Translocation rate 0.0725 0.6800 161.5

Incorporation rate 0.3550 0.8500 82.2

Figure 5.05: Regression fit analysis between experimental data and PDM,

for the pLDR-merAB3’UTR transgenic line; showing the prediction (PI) and

confidence (CI) intervals for cumulative mercury concentration.

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Mo

de

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R-Sq 99.6%

R-Sq(adj) 99.6%

Regression

99% CI

99% PI

Model = - 0.2343 + 1.077 Experimental

65

Evaluating the time behavior of cumulative volatilized mercury and the

percentage of mercury removal a better discrimination can be made. Figure 5.06

depicted this information. The cumulative volatilized data shows a big difference

(110.6%) in values, in favor of the pLDR-merAB3’UTR (Figure 5.06-A). According to the

percentage of mercury removal at the same day pLDR-merAB3’UTR data decreased but

the difference is around 0.3% (Figure 5.06-B).

Using PDM for the evaluation of a phytoremediation system provides a

comprehensive approach. Considering separately, the cumulative volatilization data

(Figure 5.06-A) and the values of rates (Table 5.03), can be stated that pLDR-

merAB3’UTR have better performance. If the amount of the mercury removed is

considered, both systems behaved in the same way. According to the PDM result, the

best phytoremediation system for this scenario is pLDR-merAB. This will be the point of

view of environmental scientist because it extracted the greatest amount of contaminant

and volatilized the least.

66

Figure 5.06: Performance comparison between transgenic lines. (A)

Cumulative volatilized mercury. (B) Percentage of total mercury removed.

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67

5.3. Capability of the PDM to Model different Phytoremediation Systems

The model has been validated with phytovolatilization data but, it can model

three more sub-divisions of phytoremediation, using different combination of calibration

as shown on Table 5.04. PDM provides interdisciplinary approach for the environmental

management discipline which includes temporal analysis and standardized protocol for

the performance evaluation. This evaluation includes comparison between plants

(species or genetically modified ones) and contaminant concentration behaviors.

Table 5.04: Auxiliary variables values to model different phytoremediation process.

Sub-division Extraction Translocation Incorporation Volatilization

Phytovolatilization Calibrated Calibrated Calibrated Calibrated

Phytoextraction

(including

accumulation on leaf)

Calibrated Calibrated Calibrated Zero

Phytoextraction Calibrated Calibrated Zero Zero

Rhizofiltration Calibrated Zero Zero Zero

To demonstrate the PDM capability to model different phytoremediation system,

the peer reviewed experimental data published by Sundberg et al. (2003) has been

used. In this article they analyzed the phytoextraction capability of tobacco plants from a

solution contaminated with perchlorate. This environmental contaminant is a concern for

federal agencies, particularly associated with drinking water. It is a very persistent

contaminant and can be introduced by natural or anthropogenic factors (Urbansky and

Schock 1999; Urbansky 2002).

68

Sundberg et al. (2003), shows a comprehensive analysis about the

phytoextraction capability of tobacco plant as a function of two concentrations and

physiological plant sections. They also evaluated the concentration in the physiological

section as a function of time. In this the data of 25ppm of contaminant concentration,

solution and leaf time behavior graphs (Figure 5.07) section will be used. These graphs

were selected because represent the contaminated media (solution) and the end point

(leaf) that can be achieved by the contaminant in this system. The phytoextraction

experiment was performed during 13 days and in duplicates.

Figure 5.07: The distribution of perchlorate amended in leaf and

depletion from nutrient solution (Adapted from Sundberg et al. 2003).

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Pe

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69

The cumulative tissue concentration was used as the estimated values of the thresholds.

To mimic the phytoextraction process the Volatilization rate was valued as zero, as

suggested in Table 5.04. All of the other variables depicted in Table 5.05 were

calibrated to obtain a qualitatively similar graph for the solution and leaf contaminant

concentrations, as time evolved. Figure 5.08 depicts the time behavior of the

experimental data and PDM, in which can be observed a good qualitatively agreement.

To evaluate the statistical significance of this agreement a Sign test was performed,

which demonstrated that the null hypothesis can be rejected with a significant

confidence level of 95%, having a median of the difference of 0.0100 (solution)

and0.3317 (leaf) mg of ClO4, both has a p-value of 0.0001 (Table 5.06). Figure 5.05

illustrated regression fit analysis in which a strong correlation (96.9%) between PDM and

the experimental data, also all data achieve the prediction interval of 99%.

Table 5.05: Auxiliary variable categorization and base scenario values.

Name Category Base scenario value

Root threshold Estimated 1.10 mg ClO4

Shoot threshold Estimated 0.80 mg ClO4

Leaf threshold Estimated 1.0 mg ClO4

Extraction rate Calibrated 0.2449 mg ClO4/(d* mg ClO4 in solution)

Translocation rate Calibrated 1.4680 mg ClO4/(d* mg ClO4 in root)

Incorporation rate Calibrated 7.0257 mg ClO4/(d* mg ClO4 in shoot)

Fraction Calibrated 0.10

70

Table 5.06: Sign test for a confidence level of 95%, testing: median = 10.00 versus

median ≠ 10.00

N Below Equal Above P Median

Solution 14 14 0 0 0.0001 0.0100

Leaf 14 14 0 0 0.0001 0.3317

Figure 5.08: Comparison between experimental data (Sundberg et al.

2003) and PDM, for the distribution of perchlorate amended in leaf and

depletion from nutrient solution.

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Pe

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mg

)

Solution-Experimental

Leaf-Experimental

Solution-PDM

Leaf-PDM

Variable

71

These results proved that PDM has the capability to simulate with a strong

correlation other phytoremediation process, besides phytovolatilization it also illustrated

the plasticity to work with different contaminants, beyond mercury.

5.4. General Discussion

The Phytoremediation Dynamic Mode (PDM) has been proved its standing to be

a mathematical model assessment tool in the environmental science field. With its

implementation the environmental science community can evaluate different

environmental interactions of the phytoremediation system, such as: contaminant (types

Figure 5.09: Regression fit analysis between experimental data and

PDM, showing the prediction (PI) and confidence (CI) intervals for

cumulative mercury concentration, for the distribution of perchlorate with

initial concentration in solution of 25 ppm.

20151050

25

20

15

10

5

0

Experimental

PD

M

R-Sq 96.9%

R-Sq(adj) 96.6%

Regression

99% CI

99% PI

PDM = 0.3008 + 0.8569 Experimental

72

and concentration), plant types (species or genetically modified) and phytoremediation

processes. PDM also, can be implemented as standardized tools for phytoremediation

systems performance evaluation. This is focused in the environmental management as

a continuous assessment tool based on a total quality management approach (Marazza

et al. 2010). The drawbacks can be answered by the implementation of PDM for the

different phytoremediation systems, through the statistical correlations between both

data sets.

The metal bioavailability has been modeled successfully by PDM, determining its

dependence of contaminant concentration. The Fraction auxiliary variable which

conglomerated the bioavailability of the contaminant summarized the root soil

dependence, has been the exponent factor of the contaminant dependence. This

variable synthesizes the soil’s physical and chemical factors, such as: pH, organic

matter, carbonates, electrical conductivity and grain distribution, which govern the

contaminant bioavailability (Almendras et al. 2009; Rodríguez et al. 2009; Zhang et al.

2010; Liu and Liu 2011). As the Fraction variable increases the contaminant has less

mobility.

The threshold values for each physiological section established in PDM can

address the drawback portion of the contaminant kept in the plant section (root, shoot,

leaf). The final concentration of contaminant by physiological section are consistent with

the typical experiment of contaminant concentration accumulated (Yu et al. 2001; Jadia

and Fulekar 2009; Sarma 2011). Taking into account the previous knowledge about

plant contaminant tolerance, the final concentration obtained by PDM, can advocate if

the phytoremediation agent can survive during the phytoremediation process. The PDM

has the capability to appraise the rates according to the physiological process: extraction

73

(soil to root), translocation (root to shoot), incorporation (shoot to leaf) and volatilization

(leaf to atmosphere).

Although the benefits of phytoremediation in comparison with traditional cleanup

techniques, EPA has concerns with regard to the best plant species for a particular

metal, and the time required for cleanup (EPA 2000; Chaney et al. 2007). Several

mathematical approaches have been implemented to understand the soil-plant

interaction (including phytoremediation) during the last forty years (Benbi and Nieder

2003). Various mathematical algorithms have been applied. Besides the System

Dynamic Approach (SDA), it has been found that the theoretical point of view provides

the differential equation solution set, defined by models for compartmentalization of the

plant and a variety of other approaches to understand the phytoremediation phenomena

in a comprehensive way (McCutcheon and Schoor 2003; Robinson et al. 2003; Trapp

2004; Thomas et al. 2005; Japenga et al. 2007; Qu et al. 2010). These models are

mathematically intensive and very specialized. Also a SDA using STELLA (system

thinking software of isee ystems) has been implemented (Ouyang 2002; Ouyang et al.

2007; Ouyang 2008). These implementations have considered an excessive complexity,

having 30 to 43 variables per model. Those variables have to be: calibrated, estimated

and assumed. The phytoremediation dynamic model (PDM) is presented as a unifying

model according to the classical plant physiology structure, providing an understandable

and comprehensive tool; representing the plant as a pipeline structure with 17

parameters, which only eight need to be: calibrated, estimated and assumed.

Modeling allows the analysis of different scenarios, and determines and ponders

the most relevant criteria to assess system performance (Fisher 2007); these features

are highly desirable for the environmental decision making process. This was

74

demonstrated for Phytoremediation Dynamic Model (PDM), which has the capability to

mimic a phytoremediation processes (phytovolatilization, phytoextraction). Also, the

fundamental assumptions of the model structure which theorized the plant physiological

behaviors as a system composed with stock (level variable) and flows (rate) was

validated, concurring with findings reported by Sundberg et al. (2003) and Hussein et al.

(2007).

The typical experimental setup approach found on metal phytoremediation fields

determined that the physiological system has a time lag of the order of days, according

to contaminant concentration processed (Yu et al. 2001; Jadia and Fulekar 2009; Sarma

2011). This effect has been observed in different bioremediation systems and has been

explained as a resilience adaptation time of the organism in a new environment with a

toxic substance (Schnoor et al. 2002; Caudill 2003; Braeckevelt 2011). This behavior is

also observed in the phytovolatilization mercury data (Hussein et al. 2007) and was

mimicked successfully by PDM. On the perchlorate experiment this phenomenon is not

observed (Sundberg et al. 2003) and PDM reproduced successfully this system also.

The variable sensitivity analysis depicted that the extraction rate is the

constringent factor on the phytoremediation process. This result corresponds with the

literature which considers the root-zone interaction, as constringent factor (Benbi and

Nieder 2003; McCutcheon and Schnoor 2003). The PDM also provides the opportunity

to analyze the flow rates of different phytoremediation systems. This kind of analysis

provides environmental managers more information to enhance the decision making

process, such as the example discussed about the differences between two genetically

modified plants (Plant type determination page 67). The statistical analysis shows that

PDM has the capability to mimic the behavior of the experimental data obtained by

75

Hussein et al. (2007) for HgCl2 phytovolatilization remediation process and, assess

which plant is better phytoremediation system according to the scenario. Also, the PDM

plasticity to simulate phytoremediation systems was shown by modeling phytoextraction

process of perchlorate contamination in solution (Sundberg et al. 2003).

5.5. Concluding Remarks

The Phytoremediation Dynamic Model (PDM) has been validated qualitatively,

quantitatively and proven statistically to have the capability mimic the behavior of

phytoremediation experimental data (phytovolatilization). The model has been validated

quantitatively in terms of two responses, volatilization rates and cumulative volatilized

contaminant. Also, it has the capability to explain more than 95% of the experimental

data values, proving the robustness of the model’s schematic structure (Forrester

diagram) and the validity of the established assumptions. The fundamental assumptions

are: (1) fluxes depend on the contaminant concentration of the previous stocks (level

variables) and the existence of the threshold concentration which allows the contaminant

flow to the next stocks; and (5) contaminant bioavailability depends as exponential ratio

between the current and initial contaminant concentration on soil, having as an exponent

the fraction variable. The non-parametric Sign test shows proved that PDM does not

differ in more than 10 units of the experimental data, having a p-value of 0.0001.

Extraction was identified as the most influent factor of the PMD response, according the

sensitivity analysis evaluated through Krustal Wallis and Tukey Tests.

The schematic representation of PDM facilitated the comprehensive

understanding of the phytoremediation process. The model can be used as a teaching

learning tool for regulatory entities, to explain the system behavior, filling the gap of the

decision making process, evaluating different possible settings. This approach will

76

provide a common ground of knowledge between regulatory entities and the community,

to leverage all group participation.

Phytoremediation Dynamic Model provides to the scientific community the

capability to make comparisons between: contaminant, contaminant concentration, plant

types (species or genetically modify) and phytoremediation processes. Assessing this

information, environmental managers can better understand the system’s behaviors and

can make more informed decisions to recommend to the regulatory agencies or select

the best approach to attend the environmental issue. Also, to the plant biotechnology

scientific community it provides the opportunity to evaluate the differences on the

physiological process as a function of time and gene manipulation.

5.6. Limitations of the Study

When a computational model is being developed, modelers need to solve issues

related to scales, determinism, parameterization and validation (Benbi and Nieder 2003).

The scale issues in PDM are represented in the plant age and roots extensions. Those

parameters will be intrinsic to the model according to the peer reviewed validation data

(e.g. Sundberg et al. 2003; Hussein et al. 2007). Phytoremediation Dynamic Model

(PDM) is a deterministic model; it assumes that each plant will interact with the

contaminant in the same way. It also assumes that the parameterization of the flow

rates of each dynamic process is constant during the simulation and that the

contaminant concentration on each physiological division depends of the contaminant

concentration of the previous one.

PDM is a unifying model that can be implemented on different experimental data

which contains contaminant measurements on all physiological divisions and at least

have a one-time dependent concentration measurement. Data with these characteristics

77

can be found on published technical sources, to validate PDM (e.g. Sundberg et al.

2003; Hussein et al. 2007). The validation was performed by analyzing the results from

phytoremediation research using transgenic tobacco plants that can extract organic and

ionic mercury, and which can transform them into less toxic elemental mercury (Ruiz et

al. 2003; Hussein et al. 2007). Both tobacco plants and mercury are excellent models to

study phytoremediation and heavy metal contamination because of the abundance of

knowledge in the field. To show the PDM capability to model different phytoremediation

process the data of perchlorate phytoextraction system (Sudberg et al. 2003) was used.

78

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