1 a chemometric approach for the distribution and source identification of he...
Final Master's Exam Presentation 071615
1. Zhenfei Liang, M.S.
Advisor: Nick Basta
Environmental Science Graduate Program
Predicting Metal(loid) Phytoaccumulation
from Soil Property and Chemical Extraction
2. CONTENTS
INTRODUCTION
TRANSFER FROM SOIL TO PLANT
METAL(LOID) BIOAVAILABILITY
METHODS FOR PREDICTING PHYTOACCUMULATION
SOIL PROPERTY METHOD
CHEMICAL EXTRACTION METHOD
CONCLUSION
PERSPECTIVES
3. INTRODUCTION
Concern over metal contaminated soil
Introduction of metals into the food chain
Loss of vegetation cover induces through phytotoxicity
Cycling of metals to surface soil horizons by tolerant
plants to induce toxic effects on plants
(McLaughlin, 2001)
WHY BOTHER?
4. INTRODUCTION
Predicting plant uptake in contaminated soil
important, with regard to plant nutrition, crop
contamination, environmental quality
Measuring total metal content in soil may not
predict phytoaccumulation
5. TRANSFER FROM SOIL TO PLANT
Controlling factors: geochemical,
climatic, biological, anthropogenic
Phytoaccumulation depends upon
abundance, speciation and binding
characteristics on soil surfaces,
governed by sorption,
complexation and redox processes
(Marschner, 1995; McBride, 1995; Sauvé et al., 2000; Kabata-Pendias, 2004; Patra et al., 2004; Moreno et al., 2005; Sterckeman
et al., 2005; Rieuwerts et al., 2006; Kalis et al., 2007; Anawar et al., 2008; Römkens et al., 2009a; Rodrigues et al., 2010)
6. METAL(LOID) BIOAVAILABILITY
Available fraction ≡ fraction of total amount of contaminant
present in a specific environmental compartment, within a
given time span, either available or can be made available for
uptake by organisms from either direct surrounding of the
organism or by ingestion of food
Bioavailability ≡ contaminant absorbed into the organism
and may cause an adverse or beneficial effect in the exposed
organism
8. METAL(LOID) BIOAVAILABILITY
Bioavailability reduces uncertainty in exposure
estimates and improves risk assessment from
contaminated plants
Accurate prediction of bioavailability improve
risk assessment in terrestrial ecosystems
(Peijnenburg et al., 1997; Sauvé et al., 1998; McLaughlin et al., 2000a; McLaughlin et al., 2000b; Peijnenburg et al., 2000;
Weng et al., 2004; Dayton et al., 2006; Menzies et al., 2007; USEPA, 2007; Zhang et al., 2010)
9. METHODS FOR PREDICTING PHYTOACCUMULATION
Plant bioassay takes a long time
Prediction is hot topic, great progress, but still difficult, no
single one reliable method exists
Mechanistic models & empirical models
Soil contaminant measurement methods:
Single Chemical Extraction
Chemical Speciation (sequential extraction or spectroscopy)
Diffusive Gradients in Thin Films (DGT) (Zhang and Davison, 1995)
Pore Water (PW) (McBride et al., 1997)
Windermere Humic Aqueous Model (WHAM) (Tipping, 1998)
Free Ion Activity Model (FIAM) (Sauvé et al., 1998)
Donnan Membrane Technique (DMT) (Temminghoff et al., 2000)
terrestrial Biotic Ligand Model (tBLM) (Di Toro et al., 2001)
HOW TO MEASURE?
10. SOIL PROPERTY METHOD
Modifying effect of soil property on correlation and
multiple-regression techniques routinely used to
examine the relationship between and among soil
properties and biological endpoints
Dominant soil properties to affect
phytoaccumulation: pH, OC, CEC, clay content, and
reactive Fe, Al, Mn oxides
(Basta et al., 2005; Fairbrother et al., 2007)
11.
12.
13. SOIL PROPERTY METHOD
Soil samples: natural source (naturally uncontaminated or
contaminated), anthropogenic source (artificially spiked)
Studied metal(loid)s: As, Cd, Ce, Cr, Cu, La, Nd, Ni, Pb, Pr, Zn
Number of soils or study sites: 3 to 215
pH, OC, total content the most significant factors for prediction, pH
and OC negatively correlated with plant uptake, total content
positively correlated
Empirical methodology, overlooking other abiotic or biotic factors
besides soil property
Soil properties inherently intercorrelated, necessitating techniques to
quantify the marginal contribution of each mitigating property
14. CHEMICAL EXTRACTION METHOD
Mechanistically based, only extract very small proportion
of potential available (bioaccessible) pool
Extraction methods:
Single extraction procedure
Sequential extraction procedures
Enhancement with microscopic and spectroscopic techniques
(Peijnenburg et al., 1999; Basta and Gradwohl 2000; Peijnenburg et al., 2007)
15. Table 2. Selected relevant key literature using chemical extraction method to predict phytoaccumulation/phytoavailability.
Elements Soil Samples
Contamination
Source
Number
of Soils
or Study
Sites
Extraction Method Comparative Method References
Cd 3 topsoil samples
typical in New
Zealand
None † 3 0.05 M Ca(NO3)2, 1 M
NH4NO3, 0.01 M CaCl2, 0.05
M CaCl2, 1 M NH4OAc, 1 M
NH4Cl, 0.04 M EDTA, and
0.05 M AAAc-EDTA
Above ground parts of
wheat (Triticum aestivum
L. “Monad”.), white clover
(Trifolium repens L.
“Huia”.), lettuce (Lactuca
sativa L. “Buttercrunch”.),
carrot (Daucus carota L.
“Space Saver”.), and
ryegrass (Lolium perenne
L. “Nui”.), and carrot roots
Cd content
(Gray et al.,
1999)
Cd 11 selected typical
soil cereal growing
in South Australia
None 11 0.01 M CaCl2, 0.05 M CaCl2,
0.1 M Na2EDTA, 0.005 M
DTPA-TEA, 1 M NH4NO3, 0.02
M AAAc-EDTA, and 1 M
NH4Cl
Measuring Cd
concentration in the stem
and leaves of durum
wheat (Triticum aestivum
L. var. Excalibur)
(Krishnamurti et
al., 2000)
Cu, Zn 12 urban surface
soils within the city
of Montreal and 1
forest soil from the
Morgan Aroretum
in Canada
Naturally
contaminated
13 0.1 M HCl, 0.01 M EDTA, 1.0
M NaAc, 0.005 M DTPA, H2O,
HNO3/HClO4, AEM-DTPA,
AEM-EDTA, electrochemical
analysis, dilute salt extraction
Measuring metal
concentrations in lettuce
(Lactuca sativa) leaves
(Tambasco et al.,
2000)
Pb, As A mixed apple
orchard from
historical use of
lead
arsenate
insecticide in WA,
US
Naturally
contaminated
1 HClc, EPA 3050, Mehlich 3,
0.5 M NaHCO3,TCLP
Assessing tree (Malus ×
domestica Borkh.) growth,
measuring leave and fruit
concentration
(Peryea, 2002)
Pb, Zn, Cu, Cd 3 top soil/sediment
from metal-
contaminated
wetlands close to
mine sites in China
Naturally
contaminated
3 HCl/HClO4, 0.005 M DTPA,
0.1 M TEA
Measuring aboveground
and underground tissue
concentrations of 12
emergent-rooted wetland
plants
(Deng et al.,
2004)
Cu 30 soils were
collected from
diverse
contaminated
sites in the UK,
Chile and China
Naturally
contaminated and
artificially spiked
30 1 M NH4NO3, 0.05 M EDTA,
free Cu2+
activity, DGT
Measuring shoot and root
concentration of Elsholtzia
splendens and Silene
vulgaris
(Song et al,
2004)
16.
17. † * p < 0.05, ** p < 0.01, *** p < 0.001.
‡ Aboveground Leersia hexandra 0.688*, underground Juncus effuses 0.512*.
18.
19.
20.
21. Soil samples: 2 naturally uncontaminated, 7 naturally contaminated, 3 natural soils
(plus naturally uncontaminated or contaminated), 1 combination of naturally
contaminated and artificially spiked
Studied metal(loid)s: As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, Zn
Number of soils or study sites: 1 to 53
Extraction tests:
H2O;
Neutral salt solutions: 0.01 M CaCl2, 0.05 M CaCl2, 0.01 M Ca(NO3)2, 0.05 M Ca(NO3)2, 0.1 M LiNO3, 0.1 M
NaNO3, 0.01 M Sr(NO3)2, 1 M NH4NO3, 1 M NH4Ac, 0.1 M (NH4)2SO4, 1 M NH4Cl, 1 M
MgCl2, 0.5 M NaHCO3, 1.0 M NaAc, 0.5 M KH2PO4;
Chelating agents: 0.01 M EDTA, 0.04 M EDTA, 0.05 M EDTA, 0.02 M AAAC-EDTA, 0.05 M AAAc-EDTA, 0.1 M
Na2EDTA, 0.05 M NH4-EDTA, Mehlich 3, AEM-EDTA, EDTA-NH4Ac, 0.005 M DTPA, AEM-
DTPA, 0.005 M DTPA-TEA, AB-DTPA, CaCl2-TEA-DTPA, DTPA-TEA-CaCl2;
Weak acids: 0.11 M HAc, 0.43 M HAc, 0.2 M C6H8O7;
Strong acids: HClc, 0.1 M HCl, 1 M HCl, 0.43 M HNO3, 0.5 M HNO3, HNO3/HClO4, HCl/HClO4, HCl/HNO3,
Mehlich 1;
Others: EPA 3050, TCLP, BCR, and rhizo
CHEMICAL EXTRACTION METHOD
22. CaCl2 significantly correlated in 6 studies
NH4OAc suitable in 3 studies
NH4Cl effective in 2 studies
Cd: 0.01 M CaCl2, 1 M NH4OAc, 1 M NH4NO3, 0.005 M DTPA-TEA
Zn: 0.01 M CaCl2, 1 M NH4OAc, 0.005 M DTPA-TEA, 1 M NaNO3
Pb: 0.01 M CaCl2, 1 M NH4OAc, 0.005 M DTPA-TEA, Mehlich 3
As: 0.01 M CaCl2, 1 M NaNO3, 0.1 M (NH4)2SO4, H2O
CHEMICAL EXTRACTION METHOD
(Gray et al., 1999; Krishnamurti et al., 2000; Song et al, 2004; Meers et al, 2005; Zhang et al, 2006; Meers et al.,2007; Vázquz et al., 2008;
Zhang et al., 2010)
23. CONCLUSION
pH, OC, total content are the most
significant factors for prediction
Neutral salt extractants provide the
most useful indication
No single one prediction method is
recognized universally
24. PERSPECTIVES
Soil property method based on spiked soils, most chemical
extraction method based on natural soils, metal(loid)
availability greater in spiked soils than naturally contaminated
The ability of regression equations from spiked soils, to predict
phytoaccumulation from naturally contaminated soils, or vice
versa
Prediction ability for other sources, such as inorganic fertilizer,
organic sewage sludge, manure byproducts
(Bolan et al., 2003; Bolan et al., 2004; Basta et al., 2005)
25.
26. ACKNOWLEDGEMENTS
• M.S. Committee
Dr. Nick Basta
Dr. Roman Lanno
Dr. Jiyoung Lee
• Colleagues
Shane Whitacre
John Obrycki
Brooke Stevens
OSU ESGP
ESGP
27. Thank you for your attention
Questions and Comments?
Editor's Notes
Good afternoon everyone. My name is Zhenfei Liang. I am a graduate student in the Environmental Science Graduate Program and working with my advisor Dr. Basta in the School of Environment and Natural Resources.
The topic I will talk about today is “Predicting metal(loid) phytoaccumulation from soil property and chemical extraction”.
Here are the contents of my presentation.
First and foremost, let’s look at the importance of this study. So why should we care the prediction of phytoaccumulation?
As a matter of fact, there has always been concern over metal(loid) contaminanted soils. For instance,
The metal(loid) contaminated soils can introduce metals into the food chain.
And the vegetation cover grown on the soil may lose as a result of phytotoxicity.
Lastly, the cycling of metals to surface soil horizons can induce toxic effects on plants.
Therefore, predicting plant uptake in metal(loid) contaminated soils is important, especially with regard to plant nutrition, crop contamination, and environmental quality.
Analysis of total content may provide information on the accumulation of contaminants. However, measuring total content may not predict phytoaccumulation, since not all the forms of contaminants present are available to plants.
Actually, there is consensus in literature that the determination of only total content does not provide enough information in contaminant environmental behavior, risk assessment practices and site-specific remediation.
So why is that? Let’s look at the transfer process of metal(loid)s from soil to plant:
The transfer of metal(loid)s from soil to plant is an element flow from abiotic to biotic compartments of the biosphere, controlled by geochemical, climatic, biological and anthropogenic factors.
Phytoaccumulation of contaminants depends upon their abundance, chemical forms and binding characteristics on soil surfaces, which is governed by soil chemical processes including sorption, complexation and redox.
As shown in the right picture, metal uptake and accumulation in plants can be divided into five steps:
First: A metal fraction is sorbed at root surface;
Second: Bioavailable metal moves across cellular membrane into root cells;
Third: A fraction of the metal absorbed into roots is immobilized in the vacuole;
Fourth: Intracellular mobile metal crosses cellular membranes into root vascular tissue (xylem);
Fifth: Metal is translocated from the root to aerial tissues (stems and leaves).
In contrast to total content, the bioavailability concept is more widely used.
Before moving forward, let’s first look at availability. The available fraction is the fraction of total amount of contaminant present in a specific environmental compartment, within a given time span, either available or can be made available for uptake by organisms from either direct surrounding of the organism or by ingestion of food.
While bioavailability is the amount of contaminant absorbed into the organism and cause an adverse or beneficial effect in the exposed organism.
Different researchers try to explain the bioavailability process involved in the soil-plant system from different perspectives. For instance, Dr. McBride at Cornell University, Drs. Wang and Han at Chinese Academy of Sciences, Dr. Kabata-Pendias at the Institute of Soil Science and Plant Cultivation in Pulawy, Poland. Noteworthily, my advisor Dr. Basta in his excellent review paper “Trace element chemistry in residual-treated soil: Key concepts and metal bioavailability”, assumes that the bioavailability process is a complex process affected by a variety of abiotic and biotic processes, including adsorption onto and desorption from mineral surfaces, precipitation, release through the dissolution of minerals, and interactions with soil, plants, and microbes.
Bioavailability could reduce uncertainty in exposure estimates and improve risk assessment from contaminated plants.
And accurate prediction of bioavailability improves risk assessment in terrestrial ecosystems.
Here we are talking about the bioavailability of metal(loid)s to plants, also called phytoavailability or phytoaccumulation.
So how can we measure phytoaccumulation? Direct plant bioassy usually takes a long time, which is not a desirable way.
The prediction of phytoaccumulation has been a hot topic for years in both agricultural and environmental studies. There has been a great progress made. However, prediction is still very difficult and no single one reliable method exists.
The current existing prediction methods can be classified into two groups: methanistically based and empirically based.
Mechanistic models, describing such rhizosphere-plant interactions, are rather difficult to develop due to the complexity of processes involved both at the soil-root interface as well as those regulating the internal translocation of contaminants to aboveground tissues.
As an alternative, empirical models are used to link the chemical availability of contaminants in soil to internal levels in plants correcting for common soil properties.
The current existing soil contaminant measurement methods include:
Single Chemical Extraction
Chemical Speciation (sequential extraction or spectroscopy)
Diffusive Gradients in Thin Films (DGT)
Pore Water (PW)
Windermere Humic Aqueous Model (WHAM)
Free Ion Activity Model (FIAM)
Donnan Membrane Technique (DMT)
terrestrial Biotic Ligand Model (tBLM)
This study will focus on soil property and chemical extraction methods. First, let’s look at the soil property method.
The modifying effects of soil property on correlation and multiple-regression techniques have been routinely used to examine the relationship between and among soil properties and biological endpoints.
Dominant soil chemical/physical properties known to affect the phytoaccumulation of contaminants are soil pH, organic carbon (OC), cation exchange capacity (CEC), clay content, and reactive Fe, Al, and Mn oxides.
Here is a list of relevant key literatures using soil property method to predict phytoaccumulation/phytoavailability.
These are the correlation results, to the left of the equation is plant concentration of contaminants, to the right of the equation are correlated soil properties.
Continued.
Collectively, the soil samples used in above literature include natural source (naturally uncontaminated or contaminated) and anthropogenic source (artificially spiked).
The studied metal(loid)s include As, Cd, Ce[ˈsɪriəm], Cr, Cu, La[ˈlænθənəm], Nd[ˌni:oʊˈdɪmiəm], Ni, Pb, Pr[ˌprezioˈdɪmiəm], Zn.
The number of soils or study sites ranges from 3 to 215.
pH, OC, total content are found to be the most significant factors for prediction, with pH and OC negatively correlated, and total content positively correlated.
However, there are problems involved with the soil property method, since it is empirical methodology, which overlooks other abiotic or biotic factors besides soil property.
Furthermore, some soil properties are inherently intercorrelated, which necessitates techniques to quantify the marginal contribution of each mitigating property.
In parallel with the soil property methodology, other researchers try to reach the prediction of phytoaccumulation from another direction, based on chemical extraction.
The extraction methodology is methanistically based, and it only extracts a very small proportion of a potential available (bioaccessible) pool contaminants. And their utility lies not in the magnitude of the amount of chemical extracted, but in the relationship between the amount of chemical extracted and the biological response.
The current existing extraction methods include:
Single extraction procedure
Sequential extraction procedures
Enhancement with microscopic and spectroscopic techniques
XANES (near-edge portions of the spectra)
EXAFS (extended fine-structure portions of the spectra)
Here is a list of relevant key studies using chemical extraction method to predict phytoaccumulation/phytoavailability.
Continued.
For the specific metal Cd, the correlation results are shown here, with the significant correlations highlighted in each cell.
For the specific metal Zn, the correlation results are shown here, with the significant correlations highlighted in each cell.
For the specific metal Pb, the correlation results are shown here, with the significant correlations highlighted in each cell.
For the specific metalloid As, the correlation results are shown here, with the significant correlations highlighted in each cell.
Collectively, the soil samples used in above literature include naturally uncontaminated, naturally contaminated, natural soils, and combination of naturally contaminated and artificially spiked.
The studied metal(loid)s include As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, and Zn.
The number of soils or study sites ranges from 1 to 53.
The extraction tests can be classified into H2O; neutral salt solutions; chelating agents; weak acids; strong acids; and others.
The suitability of neutral salt solutions for prediction of phytoaccumuiton is in accordance with general knowledge.
CaCl2 is significantly correlated in 6 studies; NH4OAc is suitable in 3 studies; NH4Cl is effective in 2 studies.
Best extractants for Cd are in the order of 0.01 M CaCl2, 1 M NH4OAc, 1 M NH4NO3, and 0.005 M DTPA-TEA;
best extractants for Zn are in the order of 0.01 M CaCl2, 1 M NH4OAc, 0.005 M DTPA-TEA, and 1 M NaNO3;
best extractants for Pb are in the order of 0.01 M CaCl2, 1 M NH4OAc, 0.005 M DTPA-TEA, and Mehlich 3;
and best extractants for As are in the order of 0.01 M CaCl2, 1 M NaNO3, 0.1 M (NH4)2SO4, and H2O.
In conclusion,
As for the soil property method, pH, OC, total content are the most significant factors for prediction;
As for chemical extraction mthod, neutral salt extractants provide the most useful indication;
Overrall, no single one prediction method is recognized universally.
Noteworthily, some of the soil property method are based on spiked soils, while most chemical extraction method are based on natural soils, since metal(loid) availability is greater in spiked soils than naturally contaminated;
There is research need to test the prediction ability of regression equations generated from spiked soils, to predict phytoaccumulation from naturally contaminated soils, or vice versa;
There is also research need to test the prediction ability for other contamination sources, such as inorganic fertilizer, organic sewage sludge, and manure byproducts.
OK. A portrait of mine working on Waterman Farm.
I would like to sincerely thank my M.S. committee, my colleagues, the staff in the environmental soil chemistry lab, my Chinese friends and American friends, and my roommates for their support during the past two years.
I would also appreciate for Ohio State and ESGP for the funding support.
