ELEMENTAL ANALYSIS IN HOUSE DUST USING HANDHELD X-RAY
FLUORESCENCE
_______________
A Thesis
Presented to the
Faculty of
San Diego State University
_______________
In Partial Fulfillment
of the Requirements for the Degree
Master of Public Health
with a Concentration in
Environmental Health
_______________
by
Yang Jiao
Summer 2012
iii
Copyright © 2012
by
Yang Jiao
All Rights Reserved
iv
DEDICATION
This thesis is dedicated to my grandma and mom for their constant love and support
throughout the years as I grow up; also it is dedicated to my dad and uncle who have been
financially supporting me for my education, without whom it would not have been possible.
A special dedication goes out to the four professors in Graduate School of Public
Health at San Diego State University, Dr. Eunha Hoh, Dr. P. J. E. Quintana, Dr. Zohir
Chowdhury and Dr. Richard Gersberg. Thank you so much for your able assistance,
suggestions, and guidance on my academic courses and projects that provided me with a firm
foundation for my future in the science field.
v
ABSTRACT OF THE THESIS
Elemental Analysis in House Dust Using Handheld X-Ray
Fluorescence
by
Yang Jiao
Master of Public Health with a Concentration in Environmental
Health
San Diego State University, 2012
House dust has been identified as a repository for many environmental pollutants, and
these may arise from internal sources or be transported from external sources. Of particular
interests in house dust are chemicals such as heavy metals and other non-metal elements that
pose potential harm to human health. These chemicals are particularly harmful to the health
of children. They have greater exposure to house dust and are more susceptible to the
harmful effects of heavy metals. Typical analytical methods require analysis in the
laboratory through laborious and expensive measures. In the present study, an easy and realtime analytical method was developed for trace elements in house dust, measured by a
handheld energy dispersive X-ray fluorescence Analyzer (XRF). The concentration values of
elements were compared with values from same dust sample previously measured by
inductively coupled plasma mass spectrometer (ICP-MS). Measurement was made on house
dust acquired by previous studies. In this study, 1.62 grams of house dust was the minimum
weight, and the handheld XRF was sensitive enough to detect a total number of fourteen
elements (Pb, Mo, Ni, Cr, Mn, Fe, Cu, Zn, Zr, Ag, Cd, Sn, Ba, and Hg), as compared to
twenty-five elements detected by ICP-MS. After excluding outliers, data from the handheld
XRF yielded a good prediction of concentration for the elements Fe, Pb, Mn and Zn, with
regression R2 values of 0.86, 0.83, 0.80 and 0.74, respectively as compared with
measurement made by ICP-MS. A moderate relationship between XRF and ICP-MS was
found for Co and Cu, with regression R2 values of 0.56 and 0.52, respectively. The lowest
correlation between XRF and ICP-MS was observed for Ni, Sn, Cr, Sb, Mo and Cd. In
conclusion, data from this study indicates that a hand held XRF may serve as a useful and
cost-effective analytical method for Fe, Pb, Mn, Zn, Co and Cu measurements in house dust.
From a public health perspective, this provides a better picture for health risk assessment.
vi
TABLE OF CONTENTS
PAGE
ABSTRACT ...............................................................................................................................v
LIST OF TABLES ................................................................................................................. viii
LIST OF FIGURES ................................................................................................................. ix
ACKNOWLEDGEMENTS .......................................................................................................x
CHAPTER
1. INTRODUCTION .........................................................................................................1 Study Objectives ......................................................................................................4 Objectives ................................................................................................................4 2. LITERATURE REVIEW ..............................................................................................5 Properties of House Dust .........................................................................................5 Composition of House Dust .....................................................................................5 House Dust as a Source of Exposure .......................................................................6 Source of Heavy Metals in House Dust .............................................................6 Source of Lead Exposure in House Dust ...........................................................8 Trends in Blood Lead Levels Among Children .................................................9 Other Factors of Lead Exposure in House Dust ..............................................10 Heavy Metals from Cigarette Smoke .....................................................................10 Tobacco and Metal in Dust Exposure ....................................................................11 Elemental Analysis by Different Analytical Methods ...........................................12 Spark-OES .......................................................................................................12 ICP-OES ..........................................................................................................13 AAS..................................................................................................................13 ICP-MS ............................................................................................................14 Energy Dispersive X-Ray Fluorescence (XRF) ...............................................14 Recent Elemtnal Studies Using XRF .....................................................................15 Warnings Against Application of XRF Instrument ...............................................16 Practical Safety Guidelines for Handheld Analyzer ..............................................17 vii
3. METHODOLOGY ......................................................................................................19 Collection of Samples ............................................................................................19 Sample Preparation ................................................................................................19 Sample Analysis.....................................................................................................21 Analytical Precautions ...........................................................................................27 Quality Assurance/Quality Control........................................................................29 Handling Non-Detections ......................................................................................30 Linear Regression ..................................................................................................31 Coefficient of Determination .................................................................................31 Statistical Analysis .................................................................................................31 4. RESULTS ....................................................................................................................32 Optimization ..........................................................................................................32 Elemental Concentration in House Dust by XRF ..................................................32 5. DISCUSSION ..............................................................................................................42 6. CONCLUSIONS..........................................................................................................50 REFERENCES ........................................................................................................................52
APPENDIX
OVERSIZE TABLES ........................................................................................................59 viii
LIST OF TABLES
PAGE
Table 1. Side-by-Side Comparison of Concentration Level of Cu, As, Ni, Sn, Cr and
Cd in ppm Between Using XRF and ICP-MS .............................................................24 Table 2. Side-by-Side Comparison of Concentration Level of Sb, Co, and Mo in ppm
Between Methods of Using XRF and ICP-MS ............................................................28 Table 3. Side-by-Side Comparison of Concentration Level of Pb, Fe, Mn, and Zn in
ppm Between Methods of Using XRF and ICP-MS ....................................................34 Table 4. Side-by Side Comparison of First and Second Runs for Elemental
Concentration in House Dust at Locations 119-1-01 and 119-2-01 Using XRay Fluorescence .........................................................................................................44 Table 5. Sample Dust Run at House Dust Mass Range from 0.3 g ~ 3.0 g by X-Ray
Fluorescence for Optimization of Dust Mass ..............................................................60 Table 6. Sample Dust Run at House Dust Mass Range from 1.0 g ~ 2.0 g by X-Ray
Fluorescence for Optimization of Dust Mass ..............................................................61 Table 7. Average Elemental Concentration in House Dust Samples Using X-Ray
Fluorescence (ppm) ......................................................................................................62 ix
LIST OF FIGURES
PAGE
Figure 1. Microbalance weighing sample cup with Mylar film...............................................21 Figure 2. 0.6 g sample dust before analyzing by X-ray fluorescence. .....................................25 Figure 3. Elemental concentration in ppm at house dust mass range (0.3 g ~ 3.0 g) by
XRF. .............................................................................................................................26 Figure 4. Elemental concentration in ppm at house dust mass range (1.0 g ~ 2.0 g) by
XRF. .............................................................................................................................33 Figure 5. The relationship between Pb concentration level obtained by using XRF and
ICP-MS. .......................................................................................................................36 Figure 6. The relationship between concentration level of Cu, Ni and Mo (original
and zoomed) obtained by using XRF and ICP-MS. ....................................................37 Figure 7. The relationship between concentration level of Cr, Mn, Fe and Sn using
XRF and ICP-MS.........................................................................................................38 Figure 8. The relationship between concentration level of Co, Zn, and As using XRF
and ICP-MS. ................................................................................................................39 Figure 9. The relationship between concentration level of Cd and Sb using XRF and
ICP-MS. .......................................................................................................................39 Figure 10. The relationship between concentration level of Fe, Pb, Mn, and Zn given
by XRF and ICP-MS after excluding the outliers. .......................................................40 Figure 11. The relationship between concentration level of Co, Cu, and As given by
XRF and ICP-MS after excluding the outliers. ............................................................41 Figure 12. The relationship between concentration level of Ni, Sn, Cr, Sb, Mo and Cd
given by XRF and ICP-MS after excluding the outliers. .............................................45 x
ACKNOWLEDGEMENTS
I would like to make a special thanks and deep appreciation to a former school lab
technician, Christina Meyer, who has given me much guidance and assistance on the
application of handheld X-Ray Fluorescence which allowed me to proceed and finish all my
experiment in time. A special acknowledgement goes to Dr. P.J.E. Quintana, the 2nd Member
of my thesis committee, who was on sabbatical but has constantly checked my thesis status
and provide as much help with the questions I encountered, keeping me on track. Thanks Dr.
Dale A. Chatfield in Chemistry & Biochemistry Department for willing to serve as the 3rd
Member of my thesis committee.
Thank you to Dr. Hoh for this opportunity to work with her and her guidance and
support as my Thesis Advisor and Professor at SDSU. I’d also like to thank the SDSU OEH
students who collected the dust samples for the Healthy Homes Study which made it possible
for me to use for my thesis. Also, I’d like to thank all of the great professors at SDSU, and
my classmates and friends, who have all made great impact on my school and life.
1
CHAPTER 1
INTRODUCTION
House dust is defined as “a complex mixture of biologically derived material (animal
dander, fungal spores, etc.), particulate matter deposited from the indoor aerosol, and soil
particles brought in by foot traffic” (Environmental Protection Agency [EPA], 1994). Ware
(2002) described house dust as a sink and repository for semi-volatile organic compounds
and particle-bound matter. House dust is also a long-term accumulation, trapping,
accumulating, and preserving contaminants (Cizdziel & Hodge, 2000). The composition of
house dust varies from house to house. In general, house dust is a collection of textile fibers,
decomposing insect parts, pet dander, human and animal hair, food leftovers, pollen grains,
mold spores, bacteria, skin flakes, insulation, sand, and in some cases, the dust mite and its
fecal material. House dust is comprised of particles from the natural decomposition of things
people have in their homes such as carpets, books, upholstered furniture, pillows, indoor air
pollution and outdoor air pollution infiltrating through cracks in doors and windows, and
outdoor soil and dust (Butte & Heinzow, 2002). Soil introduced from the outside through
track-in can be a major contributor to house dust (Ware, 2002). Infrequent cleaning, high
traffic levels, location of the house, type of yard, and condition of the central air system can
increase the amount of dust drawn into the indoor living space. The most significant sources
from indoor environments are found to be smoking, cooking, cleaning, and physical activity
of the occupants. Tobacco smoke and polluted air are additional sources of exposure.
Children, in particular, have been shown to have an increased likelihood of being
exposed to indoor house dust. Calabrese et al. (1989) and Barnes (1990) studied the amount
of soil young children age 1 to 3 years old ingested from Amherst, Massachusetts in 1987
with an observation period of 8 days. Elevated doses ingested per day had been reported for
manganese, vanadium and zirconium. Children’s hand-to-mouth behavior was further
studied. Barnes (1990), during the second week of the study period, found the child’s
aluminum-based soil ingestions to be 18,700 mg/day and their silicon-based soil ingestions
were 20,000 mg/day. Additional information about these children’s ingestion of soil during
2
the study period was reported in Calabrese and Stanek (1992). In a tracer element study by
Vam Wijnen, Clausing, and Brunekreff (1990), titanium and aluminum have been found in
both soil and feces of children at daycare center and campgrounds with limiting tracer
method (LTM) values of 111 mg/day and 174 mg/day, respectively. Aluminum, silicon, and
titanium as soil tracer elements had been used for quantitative estimates of soil ingestion in
normal children between the ages of 2 and 7 years (Davis, Waller, Buschbom, Ballou, &
White, 1990). Soil ingestion rates were highly variable. Mean daily soil ingestion estimates
were 38.9 mg/day for aluminum, 82.4 mg/day for silicon and 245.5 mg/day for titanium.
House dust is potentially an important and long-lived source of exposure to heavy
metals. House dust is now recognized as a significant pollutant source of heavy metals in the
urban environment. High levels of lead, cadmium, copper, zinc, nickel, iron and chromium
have been reported in house dust (Fergusson & Kim, 1991; Madany, Salim Akhter, & Al
Jowder, 1994). Some of the major sources of these pollutants have been found in
contaminated outdoor soil (Madany, Ali, & Akhter, 1987), lead paint (Jensen, 1992) and
traffic (Fergusson & Ryan, 1984).
The potential health effects of heavy elements are of public health concern (EPA,
1994). Particularly, heavy elements such as arsenic, lead, mercury, and cadmium found in
outdoor and indoor environment can cause great environmental and human health concern,
and therefore, should be incorporated in the assessment of health risk.
Some studies have found evidence of the toxicity of heavy metals in house dust
detrimental to the health of adults and children. Lead is a heavy metal long recognized as
toxic to humans. Children are at particular high risk for lead toxicity because their brains are
developing at a rapid speed, and in a stage of development, pollution can have significant
negative effects. Younger children are more likely to ingest lead in dust via hand-to-mouth
behavior, such as crawling or playing on the ground and then eating (Chandran & Cataldo,
2010). Lead-contaminated house dust is a significant contributor to lead intake among urban
children who have elevations in blood lead. In a prospective study of several hundred
children followed from birth to five years of age, a wide range of factors including social,
behavioral, biological and environmental factors were being assessed to examine the impact
of urban lead exposure (Bornschein et al., 1986). In another study conducted in 1996,
elevated blood lead levels of 205 urban children from 12 to 31 months of age were observed,
3
given the risk factors associated with exposure to house dust, soil, water and paint for at least
6 months length of time (Lanphear et al., 1996). Elevated blood lead level concentration of
children has been observed at different dust lead standards of 5µg/ft2, 20 µg/ft2 and 40
µg/ft2. A substantial proportion of children were reported as having blood lead levels at or
above 10 µg/dL at dust lead levels considerably lower than current standards. Associated
adverse health effects were described as poorer hand-eye coordination, longer reaction times,
and slower finger tapping. This in turn showed to lead to lower class standing in high school,
increased absenteeism and lower vocabulary and grammatical-reasoning scores in a study
conducted in Massachusetts between 1975 and 1978 (Needleman, Schell, Bellinger, Leviton,
& Allred, 1990). Further, lead levels below 10 µg/dL have been found associated with
impairment in Intellectual Quotient (IQ). Each increase of 10 µg per deciliter in the lifetime
average blood lead concentration was related to a 4.6-point decrease in IQ score (P = 0.004)
for children at three and five years of age (Canfield et al., 2003). At molecular levels, studies
have identified that lead may affect neurosensory processing in children, with consequent
decrease in auditory sensitivity and visual motor performance by disrupting the components
of the blood-brain barrier resulting in primary injury to astrocytes cells within the brain.
Lead has the ability to interfere with calcium function and therefore impacts the voltagegated channels, the first, second and third messenger systems and causes breakdown of the
homeostatic cellular mechanisms. These impacts are of great concern because they are
essential in modulating emotional response, memory and learning (Finkelstein, Markowitz,
& Rosen, 1998). Several extensive studies and databases have demonstrated the clear link
between low-level lead exposure during early development and deficits in neurobehavioralcognitive performance in childhood through adolescence. They also demonstrated the
presence of a number of neurotoxic and other adverse effects of lead at blood lead levels
(BPb) as low as 10 µg/dL (Rosen, 1995).
Adults can also be subject to the exposure of heavy metals in indoor house dust.
Hogervorst et al. (2007) used biomarkers of exposure (blood cadium, 24-h urinary cadmium
and blood lead level) to investigate the relations between exposure and metal loading rates in
house dust in the adult residents of an area with contaminated soil. Significantly higher
levels (a two-fold increase ) of the metal loading rate in house dust has been found associated
4
with increase in blood cadmium ( + 2.3%), 24-h urinary cadmium ( + 3.0%), and blood lead (
+ 2.0%), indicating cadmium and lead is coming through house dust.
Although, there have been a great number of studies on the concentrations of heavy
metals in house dust and street dust using different analytical methods to determine the
concentration of elements, including inductively-coupled plasma spectroscopy optical
emission spectroscopy (ICP-OES) (O’Rourke, Rogan, Jin, & Robertson, 2003), spark optical
emission spectroscopy (Spark-OES) (Shahar & Majid, 2008), inductively-coupled plasma
spectroscopy mass spectrometry (ICP-MS) (Cizdziel & Hodge, 2000; Rasmussen,
Subramanian, & Jessiman, 2001), and atomic absorption spectrometry (AAS) (Aurand,
Drews, & Seifert, 1983; Edelmann & Schweinsberg, 1995; Jensen, 1992; Lanphear et al.,
1996; Pachuta & Love, 1980; Rasmussen et al., 2001; Sterling, Lewis, Luke, & Shadel,
2000). Very few studies have been made in using handheld X-ray Fluorescence technology
as an analytical tool for measurement (Balasubramanian, Spear, Hart, & Larson, 2011; Bero,
Von Braun, Knowles, & Hammel, 1995; Cooper, 1973). In this study, the ability of a
handheld energy dispersive X-ray fluorescence spectrometer (XRF) to accurately test
elemental concentration in house dust from homes had been discussed. XRF has the
advantages of fast delivery, quick identification and precise analysis for elements from
magnesium to uranium (Mg to U) at a relatively low cost.
STUDY OBJECTIVES
The house dust samples analyzed in this study were obtained from homes previously
studied for nicotine contamination (Matt et al., 2004). Heavy metal contamination of house
dust in smoker homes was reported previously using inductively coupled plasma mass
spectrometer (ICP-MS) (Mohammadian, 2010).
OBJECTIVES
1. Optimize the house dust mass for measurement by portable XRF.
2. Determine which metals in settled house dust can be measured by a portable XRF.
5
CHAPTER 2
LITERATURE REVIEW
PROPERTIES OF HOUSE DUST
House dust often contains an assortment of particles of different sizes, surface texture,
polarity, and chemical composition (Butte & Heinzow, 2002; Mercier, Glorennect, Thomas,
& Le Bot, 2011; Weschler, 2009). Along with the physicochemical properties of the
chemicals such as volatility, lipophilicity and polarity, these unique features of particles
determines their absorption by house dust. Once absorbed, some contaminants do not
degrade over time or degrade at a relatively slower pace (Kerger, Finley, Corbett, Dodge, &
Paustenbach, 1997). According to EPA (1994), house dust is defined as “a complex mixture
of biologically derived material (animal dander, fungal spores, etc.), particulate matter
deposited from indoor aerosols and soil particles brought in by foot traffic”. Pollutants come
from activities and materials in the home or tracked onto particles and fibers from outside
may also be absorbed by house dust. House dust may contain volatile organic compounds
(VOCs), pesticides from imported soil particles as well as from direct application indoors,
and trace metals derived from outdoor sources.
COMPOSITION OF HOUSE DUST
House dust is significantly composed of dead skin fragments from human bodies,
tobacco smoke, industrial smoke, and airborne particles which may contain pollen or other
plant particles, etc. In a study conducted in eight Columbus and nine Seattle homes, carpet
dust had been identified as having Pb from 250 to 2,250 µg/g, potentially carcinogenic PAHs
ranging from 3 to 290 µg/g and PCBs from 210 to 1,900 ng/g. Dust collected from ten used
sofas contained averaged concentrations of 16.3, 37.2, and 229 µg/g for dust mite allergen,
cat allergen, and lead, respectively (Roberts & Dickey, 1995). Ware (2002) pointed out that
the quantity and composition of house dust varies greatly with seasonal and environmental
factors including the surroundings, exchange of outside air, age of the house, building
materials and their condition, and the quantity of furniture and carpets as well as their state of
6
preservation. House dust also varies with ventilation, heating systems, and the cleaning
habits and activities of the occupants or users of the house.
HOUSE DUST AS A SOURCE OF EXPOSURE
On average, people spend up to 95% of their time at their home, indoors.
Consequently, public concerns about indoor air quality have been on the rise with respect to
particulate material, volatile organic compounds and many other factors associated with the
risks of human exposure (Samet, Marbury, & Spengler, 1988). The indoor environment is
different as compared to the outside. Sources include material tracked indoors from the
outdoor environment by foot as well as compounds deposited after entry of contaminated
outdoor air. Ware (2002) suggested that indoor house dust can be viewed as source exposure
to humans via debris of smoking and combustion processes, abrasion of textiles and
installation objects such as building materials and furnishings (paper fibers, glass wool,
wood, and textile fibers), pollen, insect parts, and living organisms (bacteria, fungi, dust
mites). The occupants themselves also serve as an exposure source from their pets (hair,
feathers, skin fragments), and other sorts of materials generated from human activities. The
fine particles often come from very specific sources, such as tobacco smoke or generated by
indoor reactions (Matt et al., 2004). A wide variety of residential areas have been identified
as being repository for various kinds of pollutants contamination. Edelmann and
Schweinsberg (1995) brushed dust samples from horizontal surfaces such as windowsills,
shelves, picture frames, and cupboards and found mercury in trace concentration (<1-10
mg/kg) in private homes, compared to 10-250 mg/kg in laboratories, and 50-2,600 mg/kg in
dental offices.
Source of Heavy Metals in House Dust
Sources of heavy metals and trace elements in house dust are categorized into two
types of origins, indoor and outdoor. Indoor sources for house dust are generally cookers,
heaters, consumer products, building and furnishing material, smoking and incense burning
in the homes as well as infiltration of outdoor pollutants (Madany & Crump, 1993). An
outdoor source includes gasoline used in automobile activity near the location of the
residential home. Higher concentrations of elements in house dust as compared to street dust
7
is an indication of interior sources for these metals along with exterior sources. Ideally, these
types of sources present unique profiles for the elements which bring together data for source
apportionment analysis. Elements Zn, Cu and Cd are associated with tire wear, corrosion of
metallic parts of automobiles and are also associated with the type and condition of carpet in
households (Fergusson & Kim, 1991). Specifically, Cd comes from the color pigments such
as red, orange and yellow. Old carpets generate more Cd than new ones. Similarly it has
been found that Cu values are relevant to the proximity of busy roads; Zn is associated with
carpet wear and presence of rubber underlay in the houses (Jabeen, Ahmed, Hassan, & Alam,
2000).
On a worldwide scale, there have been a great number of studies focusing on the
sources of heavy metals in house dust. Most of these studies, generally, evaluated potential
indoor and outdoor sources for heavy metals most frequently identified through the elemental
analysis of house dust by using different kinds of techniques. Heavy metals most frequently
identified in house dust include, but are not limited to: Pb, Cr, Zn, Cd, Cu, and Ni. Davies,
Elwood, Gallacher, and Ginnever (1985), in a study near an old lead mining area of North
Wales, Great Britain, have identified that mineral components of dust with Cd, Cu and Zn as
well as lead and other trace elements were mainly derived from contaminated garden soil and
carried into homes through foot traffic. The study suggested that soil contributes little Cd,
Cu or Zn to house dust but 27% of the variabiliy in dust Pb was attributed to soil Pb.
Another study conduced at 76 sites in Bahrain using ICP-ES found high levels of toxic
metals Pb, Zn, Cd, Cr and Ni in residential indoor house dust. These heavy metals majorly
originated from fuel consumption, smoking, incense burning and street dust. The major
source of widespread heavy metal (Pb and Ni) contamination indoors were found to be from
automobile dust. With respect to Zn, Cd and Cr, indoor sources contributed more than
outdoor sources (Madany et al., 1994). Another factor being the degree of heavy metal
contamination in house dust reflects rapid pace of urbanization and industrialization in Asian
countries due to the increased automobile and factory emissions. Investigation of house
dusts sources has been conducted in the city center and from Yusung highway junctions in
the Taejon area, Korea where elevated levels of Pb, Cd, Cu and Zn have been noted.
Additionally, the age of property is found to influence Pb levels in house dusts, with older
houses (>15 years) having significantly higher concentrations than newer properties (<15
8
years) (Kim, Myung, Ahn, & Chon, 1998). Some studies have found that in the case of
house dust, some elements such as Cu, Co, As, Sb, Zn, Cd, Au and Pb are produced in the
house.
In a study of levels and sources of heavy metals in house dust sample collected from
nine selected houses of Jalil Town, Gujranwala, Pakistan. Jabeen et al. (2000) used AAS to
determine heavy metal Pb, Cd, Zn and Cu concentrations in different samples of houses.
This technique has also been used by Karak Industrial Estate (KIE), Jordan for investigation
of its dust, street dust and soil for content of heavy metals Fe, Cu, Zn, Ni and Pb after
digestion with nitric acid. The results of the analysis were used to determine major sources
and magnitude of heavy metals pollution. Similar studies testing heavy metals in house dust
and street dust have also been conducted in Riyadh, Saudi Arabia; Kavala’s region, Greece;
Tokat, Turkey and around the world using this technique (Christoforidis & Stamatis, 2009;
Tüzen, 2003).
Source of Lead Exposure in House Dust
Childhood lead poisoning is a major but preventable environmental health problem.
However, with the lower levels of exposure, it is becoming more and more difficult to
identify lead sources as a major way to protect children from risk of exposure. Several
studies show new approaches and technologies for the assessment of potential sources. A
nationwide study in France collected environmental sample data and personal information
from homes of 125 children between the ages of six months and six years old. Their blood
lead levels showed to be with greater than or equal to 25 µg/L. Lead isotope ratios were
determined using quadrupole ICP-MS and the signatures of the isotopic of potential sources
of exposure were matched with those of blood to identify the most likely sources. Lead
isotope ratios revealed that 32% of the subjects were exposed to a single suspected source
(Oulhote et al., 2011). Paints, dust, water, soil and unusual sources were pointed out as the
exposure source in respectively 7%, 37%, 5%, 49% and 1% of children for whom a single
source was identified.
Substantial evidence implicates that lead paint-contaminated house dust has become
the most common high-dose source of lead exposure of children in the home setting. Khan
(2011) concluded that while residential lead-based paint and lead contaminated dust and soil
9
are the most common sources of childhood lead poisoning, children can also be at risk if they
live with adults with a job or hobby that involves exposure to lead. Sayre, Charney, Jaroslav,
and Barry (1974) demonstrated that the ingestion of lead paint chips is questioned to be the
main source of lead exposure caused by lead ingestion from children’s mouthing activities in
the inner-city. House dust has been found in the hands of inner-city children and on the
interior household surfaces. The amounts of dust containing lead exceed that in similar
suburban settings.
An investigation conducted between March and August 1996 in North Carolina
described childhood lead poisoning attributed to vinyl miniblinds. Blood lead and
environmental sampling test results obtained from routine surveillance data reported that
exposure to vinyl miniblinds with dust lead levels of 100 µg/ft2 or more occurred in 44% of
the lead-poisoned children, aged 6 to 72 months; 27% of the children were exposed to levels
of 500 µg/ft2 or more. Vinyl miniblinds were identified to be the predominant source for 9%
of the children since it was introduced into the country 10 years ago (Norman, HertzPicciotto, Salmen, & Ward, 1997). Another study recorded that children may be exposed to
toxicants of industrial origin. Contamination of the home environment by soiled working
clothing being one. This is an important mechanism of exposure and has been associated
with the development of mesothelioma, berylliosis and chloracne. Lead dust carried home
on work clothing has become the apparent vehicle of exposure which is responsible for the
outbreak of symptomatic lead poisoning in the children of lead workers (Baker et al., 1997).
Trends in Blood Lead Levels Among Children
Children’s blood lead levels continue to decrease in the United States (Muntner,
Menke, DeSalvo, Rabito, & Batuman, 2005). However, the remaining disparities that exist
in historically high-risk groups in this country are continuing efforts to achieve the goal of
elimination of elevated blood lead levels. Earlier studies indicated that environmental,
demographic, and socioeconomic risk factors including older housing, poverty, age, and
being minority population are associated with higher blood lead levels among children
(Bernard & McGeehin, 2003). A broad array of studies worldwide have been conducted to
evaluate trends in children’s blood lead levels and the extent of blood lead testing of children
at risk for lead poisoning. Jones et al. (2009) reported that among children 1 to 5 years of
10
age, during the period of 1988 – 2004 in the United States, the prevalence of elevated blood
lead levels over 10 µg/dL decreased from 8.6% to 1.4%, presenting an 84% decline. From
1988 – 1991 to 1999 – 2004, there has also been blood lead levels drop-down in nonHispanic black, Mexican American, and non-Hispanic white children. However, the group
of non-Hispanic black children continues to represent the group that has the highest blood
lead concentration levels. A cross-sectional study conducted in 2008 reported lead poisoning
among children of Burmese refugees living in Fort Wayne, Indiana. Of which, 7.1% children
had elevated blood lead level (≥10 µg/dL) with prevalence ratio of 10.7 µg/dL (Ritchey et al.,
2011).
Other Factors of Lead Exposure in House Dust
Factors such as remodeling and energy conservation have also been reported to
reduce ventilation and increase relative humidity, dust, dust mites, molds, VOCs, and other
indoor air pollutants (Roberts & Dickey, 1995).
A longitudinal study has suggested that interior paint lead and exterior surface dust
lead accounted for 52% of the observed variation in interior surface dust lead (Pb)
concentration. Exterior surface dust lead from exterior surface scrapings has also been found
to directly impact on the concentration of interior house dust lead and children’s hand lead
content (Bornschein et al., 1986). One process that has a marked an effect on lead
concentration in dust is the redecoration of houses painted with lead-based paints.
HEAVY METALS FROM CIGARETTE SMOKE
Cigarette smoking may be a substantial source of intake of hazardous elements
known as Al, As, Cd, Cr, Cu, Pb, Mn, Hg, Ni, Po-210, Se, and Zn which are linked with
harmful effects on smokers, nonsmokers, and infants through parental smoking (Chiba &
Masironi, 1992). The tobacco plant itself is also a repository for mineral elements with
higher concentrations in older leaves and lower in younger, top leaves. Besides, cigarette
paper and filter are the main source of trace and other elements. Heavy metals have been
detected in mainstream cigarette smoke, including antimony, aluminum, copper, iron,
mercury, strontium, manganese, tin, and zinc. In the studies of Mussalo-Rauhamaa, Salmela,
Leppänen, and Pyysalo (1986), trace and heavy metal contents of Cu, Zn, Mn, Fe, Cd and Pb
11
were analyzed by AAS in cigarettes of freshly opened packs manufactured and sold in
Finland in the 1920s and between 1960s and 1980s. The mean Cd contents in cigarette
tobacco samples were 1.7 µg/g; copper, 15.6 µg/g; lead, 2.4 µg/g; Mn, 6.4 mg/g; and Fe, 0.5
mg/g, in 1960 – 1980. Given other improved techniques with lower detection limits, such as
instrumental neutron activation analysis (NAA), a method enabling detection of elements
with low detection limits, low level heavy metals and trace elements Sb, As, Cd, Zn, Na, Cl,
Br, K have been found at elevated levels in particulate material generated from cigarette
smoking (Landsberger & Wu, 1995). Cigarette papers have also been reported in the same
study containing various elements contents of higher values. The paper material of one
cigarette from the 1980s contains an average of 0.006, 2.7, 3.0, 0.4, 160, and 565 µg of Cd,
Zn, Cu, Pb, Mn and Fe, respectively. Heavy metals Cd, Zn, Cu, Pb, Mn and Fe can also pass
into the mainstream smoke.
Several literatures have documented the transportation of trace and heavy metals into
mainstream smoke. Evidence shows the transfer rate of Cd as 2.3% – 10% (Kjellstrom,
1979; Menden, Elia, Michael, & Petering, 1972), Zinc as 0.41% - 0.42% (Menden et al.,
1972; Nadkarni, 1974), Lead as 0.7% - 4.8% (Cogbill & Hobbs, 1957; Menden et al., 1972),
Mn as <0.02% (Cogbill & Hobbs, 1957) and Fe as 0.5% - 0.17% (Nadkarni, 1974) and Cu as
0.4% - 1.7% (Jenkins, Newman, Ikeda, & Carpenter, 1971). Additionally, sidestream smoke,
inhaled by nonsmokers, usually contains a significant proportion of heavy metals. Lead,
cadmium and arsenic have been detected in sidestream cigarette smoke, indicating that these
toxic elements can travel different distances in air flow. In general, smoking cigarettes
produces upwards of 4000 chemicals; another 333 chemical ingredients are added to cigarette
components. While investigating heavy metals in sidestream cigarette smoke, scientists
found arsenic usually becomes a liquid vapor while the cadmium and lead are solid
particulates. Counterfeit cigarettes have been shown to have higher concentrations of metals
than the Brand name varieties of many of these metals (Stephens, Calder, & Newton, 2005).
TOBACCO AND METAL IN DUST EXPOSURE
Indoor house dust exposure has been identified and associated with a wide range of
heavy metals in Bahrain (Madany et al., 1994), Copenhagen (Willers, Hein, Schutz,
Suadicani, & Gyntelberg, 1993), and Oman (Yaghi & Abdul-Wahab, 2004). Concentrations
12
of lead, copper, nickel, zinc and chromium were reported. In children, studies show an
association between environmental tobacco smoke exposure and blood Pb levels explained
by the contamination of house dust by cigarette ash and smoke (Willers et al., 1993). Blood
lead levels have been found to be almost twice the normal value for the children studied who
live in the houses with environmental tobacco smoke exposure.
ELEMENTAL ANALYSIS BY DIFFERENT ANALYTICAL
METHODS
Currently, a variety of analytical techniques are used for measurement of heavy
metals and other elements in house dust and street dust. These techniques comprise:
inductively coupled plasma- mass spectrometry (ICP-MS), inductively coupled plasmaoptical emission spectroscopy (ICP-OES), spark optical emission spectroscopy (Spark-OES),
inert gas fusion (IG), atomic absorption spectrometry (AAS) and X-ray fluorescence
spectroscopy (XRF).
Most of these techniques are destructive to the original sample, which are categorized
as “wet chemistry”. Only XRF is performed nondestructively. Spark-OES can be performed
with only minimal surface damage. Prior to analytical procedure, all the “wet chemistry”
technologies except XRF need samples to be dissolved and reacted with a standardized
reagent for each element of interest. Their characteristics are labor intensive, time
consuming, and less accurate than the current instrumental methods. According to the
reports in the literature (Margaret et al., 2011), XRF may be well suited to the fast, reliable,
direct determination of certain elements, if not all, at trace levels in house dust samples,
without the need to carry out any pre-concentration sample preparation step.
Spark-OES
Spark optical emission spectroscopy is a technique used for direct analysis of solid
metal samples. The procedure is relatively complicated. After the sample is prepared by
grinding to a uniform, flat area of about 1.5 cm across, it is placed in the instrument and
flooded with argon. A series of high energy sparks are created, first by ionizing the argon
and creating conductive plasma. Then the sparks melt, and excite the sample elements at the
spark point of impact. Later, the excited atoms in the plasma de-excite to a lower energy
state, emitting light at characteristic wavelengths for each element. The intensities of the
13
emissions are detected, measured, and compared to intensities for known stands to provide
quantitative results. Spark-OES has a wide detection span- all metallic elements can be
detected. The minimum detection limits are in the parts-per-million range. The sample
requirement is rather strict compared to the other methods- it must be a conductive metallic
solid with a minimum diameter of 5 mm or larger (Materials Evaluation and Engineering,
Inc., 2009). There are not many studies using this method in detecting metals in house dust.
This technique is gaining acceptance in the PGM industry for analysis of ores and flotation
tailings. Resano, García-Ruiz, Belarra, Vanhaecke, and McIntosh (2007) has used it for
determination of platinum group metals in solid samples at ultratrace levels. It has proved to
be a complicated analytical task demanding a complex, time-consuming analytical procedure
due to sample digestion.
ICP-OES
Inductively coupled plasma-optical emission spectroscopy is a technique for
analyzing the concentration of metallic elements in solid and liquid samples. Same as sparkOES, ICP-OES uses the optical emission principles of exited atoms to determine the
elemental concentration (Materials Evaluation and Engineering, Inc., 2009). Solid samples
are dissolved in an acid solvent to produce the solution for analysis. Then water is added to
dilute the solution to obtain a final specimen appropriate for analysis. Olesik (1991)
demonstrated that the inductively coupled plasma (ICP) has become the dominant source for
rapid spectroscopic multi-element analysis as a result of the advantage of low detection limits
with a wide linear dynamic range and high precision.
AAS
Atomic absorption spectroscopy (AAS) is a spectro-analytical procedure for the
qualitative and quantitative determination of chemical elements employing the absorption of
optical radiation by free atoms in the gaseous state (Welz & Sperling, 1999). It can be used
to determine over 70 different elements in solution or in solid samples. Typically, the
technique makes use of a flame to atomize the sample. In short, the electrons of the atoms in
the atomizer can be promoted to higher excited state for a short period of time (nanoseconds)
by absorbing a defined quantity of energy (radiation of a given wavelength). This wavelength
14
is specific to a particular electron transition in a particular element. Each wavelength
corresponds to only one element, which gives the technique its elemental selectivity.
AAS has the advantage of high sensitivity, good accuracy (relative error 0.1% ~
0.5%), and high selectivity, therefore it is widely used. A possible disadvantage of it is that a
resonance line source is often required for each element to be determined. So it detects one
element at a time and may be slow turnaround.
ICP-MS
ICP-MS has received growing attention during the past decades. ICP-MS provides
high sensitivity, selectivity, accurate and low detection limits often required for the
determination of trace elements and many advantages over other traditional elemental
analysis techniques such as AAS and OES (Resano et al., 2007). It is capable of trace multielement analysis, often at the part per trillion (PPT) level. ICP-MS has been used widely
over the years in fields including soil science, mining, water system and medicine. One of
the great advantages is extremely low detection limits for a wide variety of elements. ICPMS detection limits are often up to 3 orders of magnitude superior to those in ICP-OES,
primarily because there is no fundamental source of continuum mass to charge ratio ions in
ICP-MS. Some elements can be measured down to part per quadrillion range.
Unlike atomic emission spectrometer, ICP-MS spectrometer can accept both solid
and liquid samples (Materials Evaluation and Engineering, Inc., 2009). Once the sample is
partially desolvated, the aerosol moves through the instrument and is mixed with argon gas,
then is ionized, atoms excited in the hot plasma.
However, ICP-MS contains very expensive consumable item- a typical dynode
detector will last 6-18 months and cost on the order of $1500 – 2500 to place a new one
(Wolf, 2005). ICP-MS is light sensitive. Care should be taken to store spare detectors in the
dark and never expose a detector to the light while the high voltage power is on.
Energy Dispersive X-Ray Fluorescence (XRF)
XRF provides a rapid and non-destructive method for the analysis of trace and major
elements in dust samples (Materials Evaluation and Engineering, Inc., 2009). Although most
commonly known for diagnostic applications in the medical field, X-rays are the basis of
15
many powerful analytical measurement techniques, including XRF Spectrometry. The
elemental composition and concentration of a material were determined using energy
dispersive XRF. One of the greatest advantages is that it requires little or no preparation of
sample prior to analysis. Samples may be solids, liquids or powders.
This method identifies elements in a substance and quantifies the amount present of
those elements. An element is defined by its characteristic X-ray emission wavelength (λ) or
energy (E). The amount of an element present is determined by measuring the intensity of its
characteristic line. XRF utilizes activity in the first three electron orbitals, the K, L, and M
lines, where K is closest to the nucleus. Each electron orbital corresponds to a specific and
different energy level for a given element.
The current state-of-the-art in XRF is the result of advancements in technology
(particularly X-ray tubes, solid-state components, electronics, computers, and software).
Also, it is the result of application of the technology by instrument manufacturers, research
scientists, engineers, and industrial users. Now a mature technology, XRF is routinely used
for R&D, QC, production support, and regulatory compliance.
One of the disadvantages of XRF is that there are limits on the measurements. It
cannot analyze element with atomic mass less than sodium (e.g. boron, fluorine). The
normal quantitative limit is 10 to 20 ppm (parts per million). XRF also is not capable of
determining beryllium content, which is a distinct limitation when measuring alloys or other
materials that might contain beryllium.
RECENT ELEMENTAL STUDIES USING XRF
There have not yet been many studies using XRF for detection of elements and heavy
metals in house dust. Most studies have used AAS, ICP-MS, and other techniques which is
expensive, complicated and time-consuming. For decades, portable XRF analyzers have
been available for the in situ, non-destructive, measurement of lead in paint. Recent
advances made possible their use for analysis of airborne dust filter samples, soil, and dust
wipes. Several studies have confirmed that portable hand-held XRF is a reliable method for
the analysis of on-site lead paint as well as lead dust wipes.
Nevertheless, field portable methods are needed in risk characterization, assessment
and management to determine metal concentrations in environmental samples (Clark,
16
Menrath, Chen, Roda, & Succop, 1999). Research with lead house dust wipe samples has
also observed promising results. The field portable XRF instrument appears to be useful for
the identification of exposure in a wide variety of settings including home and industrial
regions.
Handheld XRF spectroscope was also used to measure lead-paint concentration on
specific surfaces such as walls, window sill and floors built in pre-1950 homes
(Balasubramanian et al., 2011). All of the 147 surfaces evaluated by XRF, revealed lead
concentrations above the instrument’s limit of detection (LOD). Of these 147 samples, 29
(20%) revealed detectable levels of surface lead as indicated by wipe sampling. The
statistical significance of the difference between the average lead concentrations in
windowsills, doors, wall side panels, and floors indicated that their lead concentrations were
all similar, except for the floor.
Although there have not been many studies focusing on house dust samples using
XRF, there are a number of studies that used XRF for elemental concentration of street dust.
Street dust collected from Baoji, China, one important industrial city in Northwest China,
was analyzed for Cu, Pb, Zn, Mn and Ni by XRF and was assessed for the contamination
level of heavy metals on the basis of a different index and factors. The results indicated
elevated metal concentrations in street dust in Baoji in comparison with Chinese soil. These
concentrations of heavy metals were also compared with the reported data of other cities.
Yeung, Kwok, and Yu (2003) conducted a study of determination of multi-element
profiles of street dust using XRF. A factor analysis has been used to extract four sources for
the street dust in Hong Kong. In the study, street dust samples collected in different areas in
Hong Kong were studied for 23 elements: Na, Al, Si, Cl, Ti, Ba, V, Cr, Mn, Fe, Co, K, Ca,
Ni, Cu, Zn, As, Pb, Rb, Sr, Y, Zr and Sn. The values for the Hong Kong street dust are
commensurable with the values derived in previous investigations, except that concentration
of Cl, Ca and As was much higher in Hong Kong.
WARNINGS AGAINST APPLICATION OF XRF
INSTRUMENT
The Delta Handheld XRF Analyzer is a secure and dependable instrument when used
according to Innov-X’s recommended testing techniques and safety procedures. However,
17
this instrument can produce dangerous levels of ionizing radiation (Innov-X Systems, Inc.,
2010). X-ray tubes in Delta instruments emit a tightly collimated beam of ionizing radiation.
Prolonged exposure can cause serious illness, injury, or death. The operators should follow
the operating instructions and safety recommendations and good radiation control practices.
Only individuals and operators trained in correct operating techniques and authorized to use
X-ray producing devices, and operators who have attended an Innov-X training course and
completed any other requirements as dictated by the local regulating authority should be
permitted to use it. Improper usage may circumvent safety protections and could potentially
cause harm to the user. The radiation detected at any outside surface (excluding the Prolene,
Mylar, or Kapton window area) is below that required for an unrestricted area.
For controlling the Delta’s X-ray emissions and therefore minimizing the possibility
of accidental exposure, there is a standard safety interlock structure consisting of the feature
of software trigger lock. If five minutes elapse between tests (default time), the trigger locks
automatically and the operator must tap on the lock icon to unlock it. There are other
methods by which operators can protect themselves from X-ray exposure. Adequate
shielding is achieved by establishing a no-admittance zone sufficiently distant from the
instrument’s measurement window that allows air to attenuate the beam. Or, shielding is
achieved by enclosing the beam working area with protective panels. For instance, 1/8”
stainless steel can attenuate the beam to background levels. To prevent exposure to ionizing
radiation, all reasonable measure, including labeling, operator training and certification, and
the concepts of time, distance and shielding, should be implemented to limit radiation
exposure to As Low As Reasonably Achievable (ALARA).
PRACTICAL SAFETY GUIDELINES FOR HANDHELD
ANALYZER
Test targets can include pipes, valves, large pieces of scrap metal, soil, or any sample
large enough to be tested in place. Radiation doses vary by different scenarios. For the Xray energy emitted by portable XRF analyzers (8-60 keV region) the bone in the fingers will
absorb radiation about 3-5 times more than soft tissues, so the bone would be at an elevated
radiation risk compared to soft issues. For this reason, no person shall hold a test specimen
18
in front of the window with the fingers in the direct beam, or direct the beam at any part of
the human body (Maharaj, 1994).
According to XRF safety instructions (Innov-X Systems, Inc., 2010), at any point of
operation period, the unit should not be pointed at any person besides the operator. A test
should never start by holding the sample with the operator’s fingers or in the palm of his/her
hand. The instrument should be pointed at the sample such that no part of body (including
hands or fingers) is near the measurement window. Meanwhile, take care that during testing,
personnel are not located within three feet (one meter) of the Delta’s probe head, in the
direction of the X-ray beam. At this time, the radiation detected at user interface areas is < 5
µSv/h. Operators should be noted to wear a ring-style or a badge-style dosimeter as a general
precaution to flag any misuse of the analyzer. The best practice is to wear a ring badge on a
finger on the opposite hand used to hold the analyzer. The value the ring provides is to
validate the level of accidental radiation exposure that has been experienced. These badges
generally have a threshold of 100 µSv and are renewed monthly. Therefore it takes several
cases of misuse even to obtain a reading on a typical badge. Lastly, the Delta’s nose (with
window) should be firmly placed on the target.
19
CHAPTER 3
METHODOLOGY
The dust samples analyzed during this study were collected during a prior study of
Environmental Tobacco Smoke (ETS) from different components in house dust air, surfaces,
and wipes. Whether protective behaviors of smoking parents can reduce the risk of their
children to ETS exposure was also studied. Specifically, nicotine was analyzed in dust (Matt
et al., 2004). Study design and participant criteria were described in Matt et al. (1999), and
Matt et al. (2004).
COLLECTION OF SAMPLES
Two standardized (150 cm × 150 cm) area floor dust samples per visit were collected
with a high volume, small surface sampler (HVS3, CS-3 Inc., Sandpoint, Idaho, USA).
Some homes had a smaller area sampled, with none being less than 100 cm × 100 cm. One
sample was obtained from the living room area and the other from the infant’s sleeping room
area. Areas were carefully measured from reference points in the home to allow collection of
dust from the same area each time, without leaving any marks visible to the subjects and
other residents. Floor dust samples were collected into Teflon bottles, transported on ice,
weighed, and sieved using a 150 µm stainless steel mesh, and methanol washed to remove
large debris. The sieved dust samples were then weighed and stored in glass bottles at -20˚ C
until analysis.
SAMPLE PREPARATION
In this study, a methodology was defined and further developed to extrapolate an
optimum house dust mass in grams (g) at which point the concentration level in parts per
million (ppm) for each element starts to stabilize and remain steadily at the same level
regardless of the numbers of following runs. After the optimum weight of house dust had
been identified, the number was recorded and applied to real house dust sample preparation
upon which the experiment continued to be conducted.
20
To determine this optimal house dust mass as discussed earlier, the weight of XRF
sample cups were measured at the beginning of each individual sample test run. In this
study, double open-ended XRF sample cups in nominal 64 mm diameter with integrated
serrated edged supplied snap-on rings, and thin Mylar films were used for the experiment.
Double open-ended cells enable top sample filling in advance by pre-attachment of thin-film
sample support windows with one of two supplied snap-on rings. Snap-on rings allow
extraneous thin-film trimming close to the sample cup. Mylar is often used to generically
refer to polyester film or plastic sheet serving as seals for equalizing pressure differentials.
Pre-cut circular window film (ultralene, 0.16 Mil (4µ) thick, 2.5” (64mm) diameter) was used
in this study.
Methanol (>= 99%, ReagentPlus®), clean distilled water rinsed and later air dried
tweezers is used to place the Mylar film carefully unto the open end of the sample cup to
affix a thin-film sample support window. One of the two supplied snap-on rings is used to
attach the Mylar film to the cell neck of the open end of the sample cup nearest the outer
integrated flange. The sample cup cell is designed with unique “bead-to-indent” geometry
that creates taut thin-film sample support planes and effective seals against leakage.
To determine the net mass of house dust for each run, it requires accurate
measurement of the sampling. This is done by weighing the sample cup before (tare weight)
and after putting the amount of house dust sample in (net weight), with a balance located in
the environment with room temperature, normally used for weighing filters and dust samples,
handling micro sample amounts, and to determine the mass of airborne particulate matter.
In this study, a Sartorius premium microbalance was a high-resolution weighing
instrument used to measure both sample cup weight and house dust weight, as shown in
Figure 1. The sensitivity for most applications was better than 10 µg (micrograms). The
assembled sample cup was placed carefully inside the chamber on the weighing pan of the
microbalance. Nitrile 100% Latex-free, powder-free exam gloves (Fisherbrand®) were
applied to avoid bare hands come in contact with the sample cup to be weighed. Weighing
procedures were repeated twice following the same protocol. A total of 3 readouts were
recorded.
The following step, stainless Teflon Coated scoopula was rinsed with 75% methanol
and clean distilled water in a precautionary manner. Air dry was applied next. To avoid
21
Figure 1. Microbalance weighing sample cup with Mylar film.
errors caused by air buoyancy and deviations caused by convection currents at the surface of
the house dust samples, the sample was conditioned to the room temperature for at least 2 ½
hours. The stainless scoopula was then used to transfer the dust carefully into the sample
cup. The dust covered the Mylar window completely and was packed. The assembled cup
containing the portion of the dust sample with a presumed initial mass of 0.6 g and a thinfilm sample support window was placed carefully on the weighing pan, approximately 8
seconds before the readout on the display unit started to stabilize. The readout was then
recorded. This was repeated twice. Lastly, all final readouts were recorded. As soon as the
experiment ended, the microbalance chamber was cleaned with a slightly moistened cloth to
ensure its accuracy and to ensure no liquid entered the balance housing. Other preparation
considerations include avoiding measuring very thin samples, as this could affect results.
Samples cups preparation should contain at least 15 mm of packed samples, measured in a
safe and accurate manner.
SAMPLE ANALYSIS
A handheld XRF analyzer (Innov-X Systems, Inc., 2010) was used to perform nondestructive analysis of a series of elements in house dust samples collected from Healthy
Home Study of 2004 (Matt et al., 2004) in compliance with RoHS European Council
Directive 2002/95/EC. Because of the small portion (0.6 g) of samples placed in XRF
sample cups, it is recommended the operator use the Innov-X XRF testing stand. This allows
22
the sample to be placed onto the analysis window of the analyzer without requiring the
sample to be held by the operator where there might be risk of X-ray exposure.
The Delta XRF Workstation is comprised of two major components which includes
A-020-D Test Stand and Delta analyzer. In this configuration, the Delta is controlled by
Innov-X Delta PC Software. The open-beam handheld instrument is converted to a safe
closed-beam workstation. The XRF Workstation offers such features as portable, lightweight, as well as shielded enclosure characterized by the configuration part of a hinged lid.
It also offers a rugged and repeatable testing environment as a test chamber in which sample
test runs can be completed in either laboratory or at remote field site, however they are
designed. In this study, the experiment takes place in laboratory. An I/O cable is a standard
accessory that provides a means to transfer information into or out of the sealed analyzer and
export the current day’s testing results from XRF software data management to the PC.
Two removable Li-ion batteries providing an input power of 12 VDC are standard
accessories for the Delta. Operating parameters of the Delta XRF analyzer is 8 – 40 kV, 5 –
200 µA max.
Delta Docking Station (DDS) is key accessory. It provides three functions including
Cal check by one of two means- “On Demand” or automatically; charging internal battery in
handle; charging additional battery in auxiliary socket. The instrument’s measurement (Xray) window is placed straightly upward, fastened by side screws tightly onto the center of
the open end of the XRF test chamber.
When the unit is initially powered on, the Indicator array remains off. As the test is
conducted, array is in a flashing state. At the test’s conclusion, the array stays on
continuously until the beginning of the test. Next when the indicator array is not blinking
and is on continuously, this signifies X-ray tube’s current is set to 0.0 and is producing a
minimum level of x-rays. Additionally, internal filter wheel is closed so there is no radiation
exposure to the bystanders. Details of routine testing operations vary depending on the
selected analysis mode.
In this study, for the test sequence the instrument mode was selected as Soil 3 Beam.
The standardization of Cal check was performed. One Cal check was required for every new
series of sample test runs. A Cal check test cup (316 stainless steel coupon) is used as a
reference sample to provide a test standard for the Cal check procedure when the docking
23
station is not available. By initiating Cal check, the analyzer collects a spectrum on a known
standard (Alloy 316 Stainless Steel), and compares a variety of parameters to values stored
when the instrument was calibrated at the factory. When comparisons are within pre-set
tolerances, the unit determines that it remains properly calibrated. When Cal check is in
progress, the X-ray indicator light assembly blinks. It indicates that the X-ray tube is
energized and the filter wheel is operational. In addition, a status bar appears on the UI
display, showing the percentage completion for the measurement.
The Cal check procedure takes about 15 seconds. When Cal check is successfully
completed, testing begins. A check standard should be measured after each Cal check
(standardization), periodically throughout the day, for a minimum of one minute. This
confirms that data continues to be as accurate as possible. Elemental concentrations for
elements of interest, in the range expected, plus or minus the error on the reading, should be
within 20% of the standard value.
The standards provided with Delta instruments are contained in special XRF sample
cups. These cups have film windows (through which the soil can be viewed and analyzed)
on one side, and a solid cap on the other side. In this study, NIST Standard 2702 and 2781
are tested as standard reference materials. The standard results are presented in Table 1
below.
The next step is to analyze the sample. Prepared sample house dust (0.6 g) was
maintained in the sample cup after weighing by microbalance, shown in Figure 2. The
sample cup was then placed against the instrument’s measurement window fastened at the
open end of the test chamber with the Mylar thin-film side down. The hinged lid was closed
directly over the sample cup. Once complete, the “Start” button was clicked on PC and the
test started running. The samples were measured through its Mylar film window. Each
reading was analyzed for 60 seconds (+/- error shown is 2-sigma, 95% confidence). 3 times
running for each sample mass.
After three tests with 0.6 g of sample house dust had been completed, results were
generated and stored in PC files. Each XRF run generated several data files. Later, at the
end of the experiment the data file was exported to a Microsoft Excel file from the XRF
instrument. Next, the sample cup was taken out. Another 0.3 g of sample house dust was
added into the original cup using stainless scoopula. The tweezers and scoopula were rinsed
1.00
1.09
1.55
1.63
1.62
0.67
0.67
1.07
0.66
0.69
1.08
1.15
1.63
1.62
0.98
1.54
1.62
1.35
0.85
0.77
1.09
1.40
1.32
1.62
101-1-01
103-1-01
103-1-02
105-1-01
105-1-02
113-1-01
113-1-02
119-1-01
101-2-02
101-3-02
103-2-01
103-2-02
103-3-01
103-3-02
105-2-01
105-2-02
105-3-01
105-3-02
109-2-01
109-2-02
110-2-01
110-3-01
119-2-01
121-3-01
Cu (ppm)
As (ppm)
XRF
ICP-MS XRF/ICP-MS XRF ICP-MS XRF/ICP-MS XRF
79.00
105.50
0.75
0.00
1.52
0.00
482.00
130.00
109.70
1.19
0.00
1.14
0.00
142.67
28.33
66.53
0.43
0.00
0.52
0.00
202.00
94.67
155.50
0.61
1.67
2.96
0.56
26.67
101.00
151.00
0.67
1.67
2.46
0.68
61.67
213.67
205.30
1.04
1.67
2.03
0.82
91.33
244.33
227.20
1.08
2.33
1.41
1.65
194.00
96152.33 54030.00
1.78
819.33 3.41
240.27 16766.33
117.00
68.21
1.72
0.00
0.17
0.00
522.67
114.00
77.84
1.46
1.00
3.23
0.31
536.67
161.33
120.40
1.34
0.00
0.17
0.00
129.00
39.00
44.65
0.87
0.00
1.72
0.00
328.67
95.00
123.70
0.77
1.00
6.75
0.15
85.33
47.67
34.87
1.37
0.33
0.17
2.02
259.33
114.67
71.74
1.60
3.67
0.45
8.07
40.00
131.00
105.50
1.24
4.33
6.62
0.65
64.33
91.67
104.20
0.88
1.33
4.34
0.31
28.00
137.00
158.40
0.86
5.33
5.15
1.04
103.67
185.00
102.10
1.81
2.33
0.89
2.61
51.00
155.67
106.80
1.46
0.00
2.26
0.00
11.00
93.67
111.20
0.84
8.67
14.15
0.61
13.67
81.67
127.70
0.64
4.67
1.91
2.44
40.00
83163.33 263.10
316.09
783.33 0.17
4607.82 15816.00
134.6667 140.8
0.96
2.00
0.17
12.12
9.67
*Used for XRF analysis
*Dust Mass
(g)
ID#
ICP-MS
91.54
49.04
99.78
46.80
58.72
84.03
78.45
3463.00
73.59
115.20
41.95
58.45
66.08
47.68
25.55
44.89
30.52
55.33
32.20
19.82
41.95
80.65
61.95
112.40
Ni (ppm)
XRF/ICPMS
5.27
2.91
2.02
0.57
1.05
1.09
2.47
4.84
7.10
4.66
3.08
5.62
1.29
5.44
1.57
1.43
0.92
1.87
1.58
0.55
0.33
0.50
255.30
0.09
XRF
17.67
23.00
1.00
24.67
29.33
26.33
24.00
56.67
23.33
21.33
10.33
23.67
28.33
17.00
18.67
6.33
25.00
21.33
26.00
26.33
11.67
12.67
78.67
55.33
XRF/ICPMS
ICP-MS
17.62
1.00
24.51
0.94
8.94
0.11
24.76
1.00
25.87
1.13
23.54
1.12
58.04
0.41
51.06
1.11
7.15
3.27
6.39
3.34
4.19
2.46
2.10
11.29
5.09
5.56
1.43
11.85
5.20
3.59
5.33
1.19
11.29
2.21
8.21
2.60
6.46
4.03
4.40
5.98
5.99
1.95
5.25
2.41
5.25
14.98
24.14
2.29
Sn (ppm)
XRF
36.33
22.67
29.50
13.00
26.00
39.00
26.00
683.33
47.00
48.67
45.00
109.00
1.33
151.67
11.00
30.00
16.67
59.33
14.67
17.33
54.33
63.33
586.00
31.00
Cr (ppm)
Cd (ppm)
XRF/ICPXRF/ICPICP-MS
MS
MS
XRF ICP-MS
33.15
1.10
0.33
2.25
0.15
19.26
1.18
4.33
3.29
1.32
19.67
1.50
4.00
1.00
3.99
61.54
0.21
4.67
2.95
1.58
51.82
0.50
2.67
4.21
0.63
42.30
0.92
8.67
1.90
4.57
43.56
0.60
6.00
2.29
2.62
128.10
5.33
10.00
5.22
1.92
37.74
1.25
8.33
2.92
2.86
34.93
1.39
8.00
3.27
2.45
21.32
2.11
5.67
6.42
0.88
34.09
3.20
2.00
1.71
1.17
29.43
0.05
6.33
7.90
0.80
38.05
3.99
2.67
1.73
1.54
29.81
0.37
7.67
3.28
2.34
50.76
0.59
4.00
5.08
0.79
37.85
0.44
7.67
3.62
2.12
51.88
1.14
4.67
8.37
0.56
20.85
0.70
4.00
3.10
1.29
16.37
1.06
9.00
2.49
3.62
55.34
0.98
9.33
5.34
1.75
41.14
1.54
3.00
4.86
0.62
17.64
33.22
7.67
38.94
0.20
24.48
1.27
1.00
6.54
0.15
Table 1. Side-by-Side Comparison of Concentration Level of Cu, As, Ni, Sn, Cr and Cd in ppm Between Using XRF and ICPMS
24
25
Figure 2. 0.6 g sample dust before analyzing by Xray fluorescence.
with 75% methanol followed with clean distilled water each time after the experiment and
put into room temperature for air dry. The sample cup was capped and then shook to mix the
content. Following the same protocol, the cap was then removed and the mass was weighed
using the microbalance and the test was run by XRF. Note: dirt can accumulate on the
analyzer window. Therefore, the window was wiped and cleaned after each analysis to
ensure it was not ripped or punctured.
Results were saved automatically. Next, the same process was repeated 9 times, each
time adding 0.3 g of house dust into sample cup. Once finished for the day, test results were
saved and exported to a PC by naming the export file (or accept default name), and selecting
a destination to save to the file.
Statistical analysis was then run by Excel and scatter plot graphs were generated for
each element tested by XRF, shown in Figure 3 in the result part. Since house dust is of
solid property, parts per million (ppm) is equivalent to milligrams per kilogram (mg/kg) or
micrograms per gram (µg/g).
From the observation of the trend of each graph shown in Figure 3, it can be
concluded that elemental concentration for most of metals and elements stabilize when house
dust mass reaches approximately at 1.5 g. In other words, the concentrations level off as dust
weight amounts to 1.5 g.
To extrapolate a more accurate house dust mass as further optimization, based on the
existing number of 1.5 g of measurement, the experiment continues by adding/removing
30000
20000
Fe
10000
Ti
0
0
1
2
3
4
Concentration of Mn, Sr, Cu, Zr, Co in (ppm)
Dust Weight (g)
Mn, Zr, Sr, Cu, Co
500
Mn
400
Sr
300
Cu
200
Zr
100
Co
0
0
1
2
Dust Weight (g)
3
4
Concentration of Ti, Zn, Mo in (ppm)
Fe, Ti
40000
Concentration of Sn, Sb, Pb in (ppm)
Fe Concentration in (ppm)
26
Zn, Mo
2000
1500
1000
Zn
500
Mo
0
0
1
As
5
0
Dust Weight (g)
3
4
Concentration of Cr, Ni in (ppm)
Concentration of Se, As in (ppm)
10
2
4
Pb, Sn, Ni
100
Sn
Pb
Ni
50
0
0
1
2
3
4
Dust Weight (g)
Sb, Cr, Cd
15
1
3
150
As
20
0
2
Dust Weight (g)
150
100
Cr
50
Sb
Cd
0
0
1
2
3
4
Dust Weight (g)
Figure 3. Elemental concentration in ppm at house dust mass range (0.3 g ~ 3.0 g) by
XRF.
sample dust at an interval mass of 0.25 g. Therefore, 5 different house dust weights were
determined to be measured: 1.0 g, 1.25 g, 1.5 g, 1.75 g, and 2 g, respectively. Both
microbalance and XRF were used 3 times for measurement to ensure accuracy. Between
consecutive measurements, contents of the sample cup (house dust) were mixed by shaking
vigorously for at least 1~2 minutes, inverting for several times, and allowing the sample dust
to travel from end to end to ensure evenly mixed. From scatter plot charts, patterns with
greater discernibility for different elements, as shown in Figure 3 in the result section,
illustrated that the optimum house dust mass ranges from 1.62 g to 1.63 g. Table 7 and
Table 8 in the Appendix shows the elemental concentration level data in unit of ppm.
After the optimal house dust mass was extrapolated, a ten-time measurement of house
dust between 1.62 g ~ 1.63 g was then conducted by microbalance and elemental
27
concentration by X-ray Fluorescence again. The results involved of a relatively horizontal
line nearly paralleled with the X-axis for most of the elements. It was then determined that
the optimum house dust weight for elemental concentration measurement is between 1.62 g ~
1.63 g.
Each of the real house dust samples from Healthy Home 1 was then measured for the
mass closest to the range of 1.62 g ~ 1.63 g before analysis by X-ray Fluorescence for their
elemental concentration. The final results are shown and discussed in Table 2.
ANALYTICAL PRECAUTIONS
Extra precautionary measures are necessary to avoid the potential possibility of
inadvertently introducing trace levels of contaminants that may affect X-ray data. First of all,
the highest quality XRF results are generally obtained from prepared samples. After repeated
quartering, the sample was analyzed as outlined above. All procedures for handling were
carried out without contact with metals, which was to avoid potential cross-contamination of
the samples. Second, the Delta instrument is shipped with proprietary Innov-X data
acquisition and processing software and Windows Embedded CE® operating system. The
User Interface employs an icon-based home page graphic style. Factory calibration has been
completed on all purchased modes. Third, an energy calibration check is performed to
determine whether an FPXRF instrument is operating within resolution and stability
tolerances. The energy calibration check determines whether the characteristic X-ray lines
are shifting. If the sample does not completely cover the window, ensure that the
background surface does not contain metals or even trace levels of metals, as this may affect
the accuracy of the XRF result. The XRF may report the presence of additional metals in the
surface material.
To ensure the microbalance is giving correct readouts, the balance should not be placed in
close proximity to a heater or otherwise exposed to heat or direct sunlight to avoid the
temperature increase inside the draft shield. Avoid brief fluctuations in room temperature
and protect the balance from drafts that come from open windows or doors, and from
aggressive chemical vapors. Besides, the sample should be conditioned to the temperature
inside the balance to avoid errors caused by air buoyancy and deviations caused by
convection currents at the surface of the sample. Never use bare hands to touch samples that
1.00
1.09
1.55
1.63
1.62
0.67
0.67
1.07
0.66
0.69
1.08
1.15
1.63
1.62
0.98
1.54
1.62
1.35
0.85
0.77
1.09
1.40
1.32
1.62
*Used for XRF analysis
101-1-01
103-1-01
103-1-02
105-1-01
105-1-02
113-1-01
113-1-02
119-1-01
101-2-02
101-3-02
103-2-01
103-2-02
103-3-01
103-3-02
105-2-01
105-2-02
105-3-01
105-3-02
109-2-01
109-2-02
110-2-01
110-3-01
119-2-01
121-3-01
*Dust
Mass (g)
XRF
18.33
14.67
25.00
18.67
38.00
17.67
28.00
25.00
27.33
24.67
19.67
25.33
32.33
32.67
29.00
15.33
27.00
29.00
49.00
26.33
9.00
20.33
26.00
36.67
ICP-MS
4.64
5.83
1.26
5.92
5.37
6.38
10.89
5.50
2.12
2.02
2.66
0.98
3.23
0.82
1.92
2.48
2.08
3.81
1.67
1.21
2.52
1.86
3.81
2.72
Sb (ppm)
XRF/ICPMS
3.95
2.51
19.79
3.15
7.07
2.77
2.57
4.55
12.92
12.19
7.40
25.91
10.00
39.89
15.14
6.19
12.98
7.62
29.36
21.71
3.58
10.93
6.82
13.47
XRF
136.00
68.67
45.00
109.67
120.00
115.33
58.67
2367.33
164.67
152.67
48.67
58.00
52.00
65.33
117.00
127.67
119.33
170.33
186.00
87.33
388.00
291.00
2094.33
276.00
ICP-MS XRF/ICP-MS XRF
1.61
84.42
996.67
1.42
48.32
1086.33
1.90
23.71
808.00
2.74
40.08
882.33
2.07
58.11
754.67
2.01
57.44
1186.33
1.64
35.71
1171.67
533.10
4.44
6750.00
0.85
194.39
1225.67
0.98
155.91
1208.67
0.88
55.17
1278.00
1.75
33.14
975.67
1.22
42.48
893.33
1.70
38.41
786.33
1.18
98.90
1162.33
1.24
102.63
853.33
1.52
78.35
927.67
1.74
98.12
792.00
1.44
129.35
954.33
0.91
96.11
867.67
4.77
81.27
569.33
3.17
91.86
359.67
0.84
2493.25
6102.67
3.02
91.48
432.67
Co (ppm)
ICP-MS
1.14
3.63
1.68
2.72
2.46
1.38
1.12
130.20
0.06
0.06
7.38
0.06
4.61
0.06
0.69
1.34
1.09
1.81
0.06
0.06
4.06
3.21
0.06
0.31
Mo (ppm)
XRF/ICPMS
872.74
298.94
480.67
324.98
307.02
861.53
1046.13
51.84
22284.85
21975.76
173.10
17739.39
193.82
14296.97
1695.35
637.77
852.63
438.78
17351.52
15775.76
140.26
112.12
101711.17
1395.71
Table 2. Side-by-Side Comparison of Concentration Level of Sb, Co, and Mo in ppm Between Methods
of Using XRF and ICP-MS
28
29
are to be weighed. In addition to the effect of the temperature, the extremely hygroscopic
behavior of fingerprints left on the sample will otherwise cause considerable interference
during weight measurement.
Note that if the weighing chamber has not been opened for a relatively long period of
time, it may have a temperature different from that of the balance’s surrounding
environment. A change in temperature will inevitably occur when opening the weighing
chamber due to the laws of physics, and may show up as a change in the weight readout.
Therefore, it is recommended that before starting the actual weighing series the operator open
and close the weighing chamber at the same rate as will be doing during weighing. The
accuracy of the weight readouts will increase as continuing weighing with great consistency.
The operator can have the balance automatically display an adjustment prompt after a
certain time interval has elapsed since the last calibration/adjustment or when the ambient
temperature changes by a defined amount. The operator can also configure the balance to
perform calibration and adjustment automatically (isoCAL) when the pre-set time and/or
temperature limit is reached.
QUALITY ASSURANCE/QUALITY CONTROL
All elemental analysis techniques experience chemical and physical interferences.
They must be corrected or compensated for in order to achieve adequate analytical results.
For XRF Spectrometry, other specific elements in a substance can introduce primary
interference that can influence the analysis of the target element(s) of interest. In this thesis,
several procedures were conducted for this purpose to avoid possible interferences. Each
time house dust was weighed, it was measured three times using microbalance (Sartorius ®).
The digit accuracy of ten to the 5th was given each time during the calibration process. The
results were determined by running three consecutive analytical runs for each sample of
house dust with pre-assigned weight in grams by the experimental design. At the end of each
analytical run, XRF Analyzer instrument gives three results (1st, 2nd, and the Average). The
results of 1st and 2nd were excluded when making excel graph.
Sampling error may also occur when too few samples are collected and tested. An
incomplete picture of the extent of metals contamination may also be obtained. Methods
have been developed to reduce sampling errors by increasing the number of samples
30
measured. In general, a large number of screening-level measurements provide a better
characterization of contamination than a small number of measurements produced by sample
removal and analytical analysis. All analytical methods require a uniform, homogenous
sample for the best results.
Quality assurance consists of testing known standards to verify calibration, as well as
testing blank standards to determine limits of detection and to check for sample crosscontamination or instrument contamination. The Innov-X analyzer performs energy
calibration check automatically. This is the purpose of the standardization check when the
analyzer is started. The software does not allow the analyzer to be started if the
standardization is not completed.
To verify there is no contamination on the analyzer window or other component that
is seen by the X-rays, the clean SiO2 blank should be used as an instrumental blank, once per
day, preferably once every 20 prepared samples. Innov-X provides standard reference
samples for calibration check. A two-minute standard test should be performed upon
instrument start-up and periodically during testing, preferably every 4 hours or every 20
samples. The difference between the XRF result for an element and the value of the standard
should be 20% or less. Reference standards are generally applicable for Pb, As, Cr, Cu, and
Zn. Innov-X provides additional reference standards for Priority Pollutant metals including
Cd, Se, Ag, Hg, Ag, Ba, Sn, Sb, and Ni.
The presence of significant sample moisture impacts on XRF results by altering the
soil chemistry, because water is another chemical compound that comprises the soil matrix.
Moisture impedes the ability to properly prepare samples. It is known that moisture content
above 20 percent cause the problems. Furthermore, laboratory results are provided on a dry
weight basis. Laboratory results are provided on a dry weight basis. The Compton
Normalization or fundamental parameters methods are implemented in order to automatically
correct results for changes to the soil matrix. Therefore, soil moisture is not considered as a
significant effect on accuracy due to effects of soil matrix.
HANDLING NON-DETECTIONS
For samples having concentrations of metals and elements below the method
detection limit (MDL), the instrument by manufacture gives either negative results or zero.
31
Negative readouts given by XRF were rounded up to 0 in this study in order for plotting
graph and comparison. This was performed prior to normalizing the data to area.
LINEAR REGRESSION
Datasets were used to develop an equation (a linear regression line) for predicting a
value of the dependent variables given a value of the independent variable. A regression line
is the line described by the equation and the regression equation is the formula for the line.
The regression equation is given by:Y = a + bX where X is the independent variable, Y is the
dependent variable, a is the intercept and b is the slope of the line.
The equation for the regression line is usually expressed as Y = intercept + slope×X.
This equation can be used to predict the value of Y for a given value of X. It can also predict
X from Y, using the equation X = (Y−intercept)/slope. These predictions are best done
within the range of X and Y values observed in the data.
COEFFICIENT OF DETERMINATION
The coefficient of determination, R2, expresses the strength of the relationship
between the X and Y variables. It is the proportion of the variation in the Y variable that is
"explained" by the variation in the X variable. R2 can vary from 0 to 1; values near 1 mean
the Y values fall almost right on the regression line, while values near 0 mean there is very
little relationship between X and Y. Regressions can have a small R2 and not look like there
is any relationship, yet they still might have a slope that's significantly different from zero.
STATISTICAL ANALYSIS
In this study, initially, a correlation test was performed to determine whether there
exists a relationship between the elemental concentrations given by XRF versus that given by
ICP-MS. A series of correlation coefficient (R2 value) is then generated for each element of
interest. This was followed by performing linear regression between the two analytical
methods, to test for whether the elemental concentration data generated by XRF was
positively correlated with that of ICP-MS. Statistical analysis was performed by Microsoft
Excel 2007. An alpha value of 0.05 and confidence interval level of 95% was used to
determine significance.
32
CHAPTER 4
RESULTS
OPTIMIZATION
As shown in Figure 3, the concentration level for the elements (Fe, Ti, Zn, Mo, Mn,
Sr, Cu, Zr, Co, Cr, Ni, and As) stabilize to a steady level when house dust mass reaches at
approximately 1.5 g. In other words, the concentration levels off as dust weight amounts to
1.5 g. Exceptions were found for element Cd, Sn, Sb and Pb. There is no distinct pattern of
concentration level relative to house dust mass.
Figure 4 illustrated that when house dust mass is over a narrower range of 1.0 g ~ 2.0
g the optimum house dust mass ranges from 1.62 g to 1.63 g.
After the optimal house dust mass was extrapolated, a ten-time measurement of house
dust between 1.62 g ~ 1.63 g was then conducted by microbalance and elemental
concentration by XRF again. The results involved of a relatively horizontal line nearly
paralleled with the X-axis for most of the elements. It is then determined that the optimum
house dust weight for elemental concentration measurement is between 1.62 g.
All of the real house dust samples from Healthy Home 1 were then measured for the
mass closest to 1.62 g before analysis by XRF for their elemental concentration.
ELEMENTAL CONCENTRATION IN HOUSE DUST BY XRF
Table 5 and 6 in the Appendix present concentrations of the elements (Mo, Ni, Cr,
Mn, Fe, Cu, Zn, As, Cd, Sb, Hg, and Pb) analyzed by XRF in this study in ppm in house dust
samples (N=24). These house dust samples were previously measured by ICP-MS. In order
to compare these two different elemental analytical methods and make meaningful
interpretations of the inherent similarities and differences in results from one method to the
other, concentration in ppm was used. Due to limited amount of residual house dust samples
left from the previous study (Matt et al., 1999; Matt et al., 2004; Mohammadian, 2010), only
24 house dust samples had dust samples
Zn, Mo
10000
Fe
5000
0
0.80
Ti
1.00
1.20
1.40
1.60
1.80
2.00
2.20
Dust Weight (g)
Mn, Zr, Sr, Cu and Co concentration in (ppm)
Mn, Zr, Sr, Cu, Co
400
300
Mn
200
Zr
100
Sr
0
0.80
Cu
1.00
1.20
1.40
1.60
1.80
2.00
2.20
Concentration of As, Se, Hg in (ppm)
Dust Weight (g)
Co
As
8
6
4
2
As
0
0.80
1.00
1.20
1.40
1.60
1.80
2.00
Dust weight (g)
2.20
Pb, Sn, Ni Concentration in (ppm)
15000
Zn, Mo concentration in (ppm)
Fe, Ti
20000
1000
800
600
400
200
0
0.80
Mo
1.00
1.20
1.40
1.60
1.80
2.00
2.20
Mn, Zr, Sr, Cu, Co
Pb, Sn, Ni
150
300
100
200
50
100
Zn
Dust Weight (g)
400
Mn
ZrPb
SrSn
0
0.80
1.00 1.20 1.40 1.60 1.80 2.00 2.20
0
Dust Weight (g)
0.800.901.001.101.201.301.401.501.601.701.801.902.002.102.20
Concentration of Sb, Cr, Ag, Cd in (ppm)
Fe Concentration in (ppm)
33
Ni
Cu
Co
Sb, Cr, Cd
50
40
30
20
10
0
0.80
Sb
Cr
Cd
1.00
1.20
1.40
1.60
1.80
2.00
2.20
Dust Weight (g)
Figure 4. Elemental concentration in ppm at house dust mass range (1.0 g ~ 2.0 g) by
XRF.
sufficient for analysis by XRF. Of which, only 5 samples (ID# 105-1-01, 105-1-02, 103-301, 103-3-02, and 121-3-01) had the optimum mass of 1.62 g ~ 1.63 g. The rest of the
samples appeared more or less insufficient to the optimum mass as determined in the
methodology of this study.
A significant portion of elements being analyzed in both studies were characterized as heavy
metals. In this study, 13 metals (Pb, Fe, Mn, Cu, Zn, As, Ni, Sn, Cr, Cd, Sb, Co and Mo)
were tested for concentration by XRF. They were also previously analyzed by an ICP-MS.
Tables 1, 2 and 3 present side-by-side comparisons of concentrations in ppm for each of the
13 elements between the two methods of XRF and ICP-MS used. The accuracy of XRF
instrument itself is further discussed.
The results show that for elements Pb, Fe, Mn, and Zn, the concentration ratio
(columns highlighted in grey) of XRF/ICP-MS are within a narrow range except for two
samples (ID#119-1-01 and ID#119-2-01). The ratio of Pb measurements by two methods
1.00
1.09
1.55
1.63
1.62
0.67
0.67
1.07
0.66
0.69
1.08
1.15
1.63
1.62
0.98
1.54
1.62
1.35
0.85
0.77
1.09
1.40
1.32
1.62
101-1-01
103-1-01
103-1-02
105-1-01
105-1-02
113-1-01
113-1-02
119-1-01
101-2-02
101-3-02
103-2-01
103-2-02
103-3-01
103-3-02
105-2-01
105-2-02
105-3-01
105-3-02
109-2-01
109-2-02
110-2-01
110-3-01
119-2-01
121-3-01
Pb (ppm)
XRF
ICP-MS XRF/ICP-MS
XRF
61.33
40.57
1.51
12707.33
33.00
20.16
1.64
7782.00
14.00
8.59
1.63
4782.00
95.00
91.91
1.03
10116.33
89.67
68.24
1.31
11227.33
79.67
51.43
1.55
11898.33
100.33
53.49
1.88
11819.67
1560.00 61.08
25.54
60219.33
67.67
46.34
1.46
15187.67
72.00
51.73
1.39
14899.00
38.67
25.71
1.50
9733.33
22.33
13.77
1.62
7503.00
32.33
30.49
1.06
9251.67
16.67
10.29
1.62
7381.00
115.00
83.27
1.38
12653.33
88.33
75.50
1.17
11625.33
96.67
89.46
1.08
11061.33
88.33
93.11
0.95
15319.67
94.67
65.76
1.44
18731.00
72.67
50.08
1.45
11768.33
70.00
83.37
0.84
38970.00
66.67
79.70
0.84
33045.33
0.40
34.42
0.01
55164.33
171.67 207.00
0.83
21855.00
*Used for XRF analysis
*Dust
Mass (g)
ID#
ICP-MS
3695.00
3156.00
2617.00
5097.00
4477.00
4936.00
4316.00
8238.00
6860.00
6742.00
7372.00
6260.00
10010.00
6118.00
9182.00
12250.00
11410.00
15410.00
11980.00
7401.00
36740.00
29470.00
4123.00
14880.00
Fe (ppm)
XRF/ICP-MS
XRF
3.44
108.00
2.47
97.67
1.83
50.33
1.98
136.33
2.51
151.00
2.41
173.00
2.74
171.33
7.31
1193.33
2.21
146.67
2.21
150.33
1.32
111.00
1.20
89.00
0.92
114.00
1.21
88.67
1.38
187.00
0.95
182.67
0.97
157.67
0.99
209.67
1.56
355.00
1.59
213.00
1.06
682.33
1.12
600.00
13.38
864.67
1.47
390.00
ICP-MS
63.50
65.01
44.32
157.40
135.80
94.63
81.03
174.10
33.04
31.12
36.39
34.76
49.13
38.56
57.11
58.20
63.51
121.20
217.00
50.51
498.70
369.90
20.45
72.76
XRF/ICP-MS
XRF
1.70
687.33
1.50
845.33
1.14
234.67
0.87
818.33
1.11
1152.67
1.83
3448.00
2.11
3265.67
6.85
26162.33
4.44
916.67
4.83
752.67
3.05
1081.00
2.56
384.67
2.32
825.00
2.30
340.33
3.27
1067.67
3.14
1295.33
2.48
930.00
1.73
2412.67
1.64
1028.67
4.22
600.67
1.37
809.33
1.62
671.67
42.28
27374.67
5.36
1466.67
Mn (ppm)
ICP-MS XRF/ICP-MS
421.00
1.63
497.90
1.70
207.50
1.13
741.50
1.10
856.40
1.35
1553.00
2.22
1404.00
2.33
427.80
61.16
593.40
1.54
529.50
1.42
758.20
1.43
366.70
1.05
775.30
1.06
283.20
1.20
678.60
1.57
1042.00
1.24
802.70
1.16
2002.00
1.21
620.40
1.66
396.30
1.52
958.30
0.84
740.50
0.91
636.20
43.03
1179.00
1.24
Zn (ppm)
Table 3. Side-by-Side Comparison of Concentration Level of Pb, Fe, Mn, and Zn in ppm Between Methods of Using XRF and
ICP-MS
34
35
(concentration measured by XRF/concentration measured by ICP-MS) varied from 0.83 to
1.88 except ID#119-1-01, with ratio value of 25.54, and ID#119-2-01 with ratio of 0.01. For
Fe, Mn and Zn, the ratios varied from 0.95 to 2.74, 1.11 to 5.36, and 0.91 to 2.33,
respectively. The ratios in the samples, ID#119-1-01 and ID#119-2-01 for Fe, Mn, and Zn
were also extremely out of range, either too high or too low. The ratio for Fe in ID#119-2-01
is 13.38, for Mn is 42.28, and for Zn, the ratios in ID# 119-1-01 and ID#119-2-01 reached as
high as 61.16 and 43.03, respectively. Similar trend was observed for Cu, As, Ni, Sn and Cr.
Extremely high XRF/ICP-MS ratios were found in ID# 119-2-01 for Cu, As, Ni, Sn and Cr,
with values of 316.09, 4607.82, 255.30, 14.98 and 33.22, respectively. In ID#119-1-01, the
ratio for element As is as high as 240.27, while it is observed non-detectable in 7 samples
including ID#101-1-01, ID#103-1-01, ID#103-1-02, ID#101-2-02, ID#103-2-01, ID#103-202, and ID#109-2-02. The ratios for Cu, Ni, Sn, Cr are within the normal range. The ratio
for Cu varied from 0.43 to 1.81; Ni from 0.09 to 7.10; Sn from 0.94 to 5.56; Cr from 0.50 to
5.33, and element As from 0.15 to 2.44, with one exception, ID#121-3-01, reaching as high
as 12.12, and for sample ID#101-1-01, ID#103-1-01, ID#103-1-02, ID#101-2-02, ID#103-201, ID#103-2-02, and ID#109-2-02, concentration of As are non-detectable. Despite the
variation of ratios observed for Cu, As, Ni, Sn and Cr, the ratios for element Cd were found
within a narrow range from 0.15 to 4.57.
XRF/ICP-MS ratios were found significantly high for all samples for Sb, Co, and Mo,
with Mo the highest among all. The ratio for Sb ranged from 2.51 to 39.89, Co from 23.71 to
129.35, and Mo from 112.12 to 22284.85. Ratios of Co and Mo in ID#119-2-01 reached as
high as 2493.25 and 101711.17. Ratios in ID#119-1-01 showed no distinct variation for all
three elements.
Later, this was followed by a linear regression test of significance. The data for each
element was plotted as a scatter of points on two-dimensional graphs, as presented in Figure
5 to Figure 8. Concentration levels were given by ICP-MS in ppm on the X axis and
concentration levels in ppm were given by XRF on the Y axis. A linear regression equation
was obtained for each line that best fit the data points, and used to determine whether the
slope of this line is significantly different from zero. This line serves as a visual summary of
the relationship between these two variables (the results given by ICP-MS and XRF).
Concentration by X‐ray Fluorescence (ppm)
36
Pb
2000
y = 0.8672x + 79.26
R² = 0.0135
1500
1000
500
0
0
50
100
150
200
250
Concentration by ICP/MS (ppm)
Figure 5. The relationship between Pb concentration level obtained
by using XRF and ICP-MS.
For Pb, the clustering of the data points along the trendline suggests a strong
(positive) correlation between analytical methods of ICP-MS and XRF, as can be seen from
Figure 5, most of the data points, except ID#119-1-01, clustered around the best-fit line (in
red). This corresponds with the high XRF/ICP-MS ratio (25.54) for Pb in ID#119-1-01. The
“coefficient of determination” (R2 value) suggests that 1.35% of the variation in Pb
concentration level given by XRF is explained by the variation given by ICP-MS. Thus, the
equation y = 0.8672x + 79.26 yield poor predictions of the corresponding value based on the
known value. When XRF value is known, y = 0.8672x + 79.26 does not give good
prediction of ICP-MS value. Later, this outlier was excluded from the data points. Graphic
results are shown in the discussion part as Figure 9 to 11.
The scatter plots for Cu and Ni appeared abnormally distributed. This may result
from the two single outliers that are distributed unusually far from the rest of the data points.
One of the single outliers detected from the scatter plot graphs corresponded with the high
XRF/ICP-MS ratio found in ID#119-2-01 for Cu and Ni, as were shown earlier in Table 1.
After zooming the scatter plot for Cu, a positive linear regression was observed. Similar
patterns were observed for Ni. Before zooming the scatter plot, it was shown that two
outliers in ID#119-1-01 and ID#119-2-01, greatly affected the linear regression line for Ni.
After zooming for a closer look at the rest of the data points, a positive linear regression was
shown in the plot. However, for the element Mo, there was no linear regression trend shown
37
y = 1.7187x + 3511.1
R² = 0.5565
20000
40000
Concentration by ICP/MS (ppm)
60000
Ni
20000
y = 4.6816x + 548.4
R² = 0.5087
15000
10000
5000
0
0
1000
2000
3000
4000
Concentration by X‐ray Fluorescence (ppm)
Concentration by X‐ray Fluorescence (ppm)
0
Concentration by X‐ray Fluorescence (ppm)
Concentration by X‐ray Fluorescence (ppm)
Cu
120000
100000
80000
60000
40000
20000
0
Cu
300
250
200
150
100
50
0
y = 1.7187x + 3511.1
R² = 0.5565
0
100
200
Concentration by ICP/MS (ppm)
Ni
600
500
400
300
200
100
0
y = 4.6816x + 548.4
R² = 0.5087
0
Concentration by ICP/MS (ppm)
50
100
150
C
Concentration by ICP/MS (ppm)
8000
y = 42.878x + 1073.8
R² = 0.5116
4000
2000
0
0
50
100
Concentration by ICP/MS (ppm)
200
Mo
150
Concentration by X‐ray Fluorescence (ppm)
Concentration by X‐ray Fluorescence (ppm)
Mo
6000
300
2000
y = 42.878x + 1073.8
R² = 0.5116
1500
1000
500
0
0
2
4
6
8
10
Concentration by ICP/MS (ppm)
Figure 6. The relationship between concentration level of Cu, Ni and Mo (original and
zoomed) obtained by using XRF and ICP-MS.
for XRF and ICP-MS values. The majority of data points for Mo irregularly
distributed throughout the scatter plot. The ratio of XRF/ICP-MS was extremely high (<=
22284.85) in every location. Therefore, a R2 value of 0.5116 has no significance in
determining the correlations between XRF and ICP-MS values. The equation does not yield
good prediction for concentration by ICP-MS when given by XRF.
The linear regression of concentration levels for Cr, Mn, Fe and Sn were shown in
Figure 7, illustrating relatively moderate positive correlation prior to excluding the outliers
in the scatter plots. The R2 value for Cr was 0.2744, the highest among all four elements. It
was interpreted as approximately 27.44% of the variation in Cr concentration by XRF was
explained by the variation by ICP-MS. A positive linear regression was observed in the
graph. Two outliers were ID#119-1-01 and ID#119-2-01, corresponding with the high
38
Cr
y = 3.9407x ‐ 65.875
R² = 0.2744
300
Cr
200
Linear (Cr)
100
0
0
100
200
300
400
Concentration by X‐ray Fluorescence (ppm)
Cr concentration by ICP/MS (ppm)
Fe
60000
50000
y = 0.6783x + 11119
R² = 0.1424
40000
30000
20000
10000
0
0
10000 20000 30000 40000 50000 60000
Concentration by ICP/MS (ppm)
Concentration by X‐ray Fluorescence (ppm)
500
400
Mn
1200
y = 1.2207x + 145.33
R² = 0.2423
1000
800
600
400
200
0
0
200
400
600
800
1000
Concentration by ICP/MS (ppm)
Concentration by X‐ray Fluorescence (ppm)
Cr concentration by X‐ray Fluorescence (ppm)
600
Sn
80
70
y = 0.357x + 20.271
R² = 0.099
60
50
40
30
20
10
0
0
20
40
60
80
Concentration by ICP/MS (ppm)
Figure 7. The relationship between concentration level of Cr, Mn, Fe and Sn using
XRF and ICP-MS.
XRF/ICP-MS ratios of 5.33 and 33.22, respectively. The second highest R2 values were
found for Mn, prior to excluding two outliers, ID#119-1-01 and ID#119-2-01, with a R2 value
of 0.2423. Similarly, a positive linear regression trend was observed. A great majority of the
data points were grouped along the best-fit line. Two distinct outliers, ID#119-1-1 and
ID#119-2-01, for Mn corresponded with high XRF/ICP-MS ratios of 6.85 and 42.28,
respectively. The next highest was followed by element Fe, with a R2 value of 0.1424. The
outliers, ID#119-1-01 and ID#119-2-01, did not have a tremendous effect on the existing
trendline; a clear positive linear regression line was shown in the scatter plot. The outliers
for Fe were found at 119-1-01 and 119-2-01, corresponding to the XRF/ICP-MS value of
7.31 and 13.38, respectively, shown in Table 3. The lowest R2 value among the four
elements was discovered for Sn, as low as 0.099. Data points randomly distributed
throughout the scatter plot, showing neither distinct positive nor negative linear regression
trend.
Concentration by X‐ray Fluorescence (ppm)
39
Co
15000
y = 3.0722x + 725.73
R² = 0.0189
10000
5000
0
0
2
4
6
8
10
Zn
30000
25000
20000
15000
10000
5000
0
Concentration by X‐ray Fluorescence (ppm)
Concentration by X‐ray Fluorescence (ppm)
Concentration by ICP/MS (ppm)
y = ‐1.3095x + 4281.6
R² = 0.0058
0
500
1000
1500
2000
Concentration by ICP/MS (ppm)
2500
As
1000
800
600
400
200
0
y = ‐5.4364x + 82.972
R² = 0.0057
0
5
10
Concentration by ICP/MS (ppm)
15
Cd
12
10
8
6
4
2
0
y = 0.0648x + 5.139
R² = 0.0299
0
20
40
Concentration by ICP/MS (ppm)
60
Concentration by X‐ray Fluorescence (ppm)
Concentration by X‐ray Fluorescence (ppm)
Figure 8. The relationship between concentration level of
Co, Zn, and As using XRF and ICP-MS.
Sb
60
50
40
30
20
10
0
y = ‐0.5784x + 27.594
R² = 0.0248
0
5
10
Concentration by ICP/MS (ppm)
15
Figure 9. The relationship between concentration level of Cd and Sb using XRF
and ICP-MS.
Fe
Pb concentration by X‐ray Fluorescence (ppm)
Fe concentration by X‐ray Fluorescence (ppm)
40
50000
y = 0.8955x + 5089.6
R² = 0.8579
40000
30000
20000
10000
0
0
10000
20000
30000
Pb
200
y = 0.7852x + 24.296
R² = 0.8275
175
150
125
100
75
50
25
0
40000
0
Mn
800
y = 1.2395x + 73.758
R² = 0.8041
700
600
500
400
300
200
100
0
0
100 200 300 400 500 600 700 800
Mn concentration by ICP/MS (ppm)
50
100
150
200
250
Pb concentration by ICP/MS (ppm)
Zn concentration by X‐ray Fluorescence (ppm)
Mn concentration by X‐ray Fluorescence (ppm)
Fe concentration by ICP/MS in ppm
Zn
3500
y = 1.6709x ‐ 184.11
R² = 0.7408
3000
2500
2000
1500
1000
500
0
0
500 1000 1500 2000 2500 3000 3500
Zn concentration by ICP/MS (ppm)
Figure 10. The relationship between concentration level of Fe, Pb, Mn, and Zn given
by XRF and ICP-MS after excluding the outliers.
As shown in Figure 8, the outlier effect is significant for all three elements Co, Zn
and As. Prior to excluding the outliers, ID#119-1-01 and ID#119-2-01, it was shown in the
scatter plots that most of the data points given by XRF measurement seem to be nondetectable. The scatter plot for Co showed that the majority of the data points were located
closely at the X-axis except for ID#119-1-01 and ID#119-2-01. There was no positive linear
regression or negative linear regression observed for Co. Its R2 value was as low as 0.0189.
For element Zn, the scatter plots were irregularly distributed. A negative linear
regression was observed for the rest of the data points when there were outlier effects. An R2
value for Zn was 0.0058 less than that of Co. For element As, the R2 value was even lower,
0.0057. No linear regression was observed prior to excluding the outliers, ID#119-1-01 and
ID#119-2-01.
The scatter plot for Cd did not illustrate a linear regression overall. Most of the data
points clustered together were within a narrow range (below 8 ppm); one outlier, ID#119-201, was observed. R2 value was as low as 0.0299. With regard to element Sb, as could be
Co
450
400
350
300
250
200
150
100
50
0
Cu
Concentration by X‐ray Fluorescence (ppm)
Concentration by X‐ray Fluorescence (ppm)
41
y = 71.104x + 5.9299
R² = 0.5614
0
2
4
6
Concentration by X‐ray Fluorescence (ppm)
Concentration by ICP/MS (ppm)
300
y = 0.808x + 25.221
R² = 0.515
250
200
150
100
50
0
0
50
100
150
200
Concentration by ICP/MS (ppm)
250
As
10
9
8
7
6
5
4
3
2
1
0
y = 0.4964x + 0.5509
R² = 0.5151
0
5
10
15
Concentration by ICP/MS (ppm)
Figure 11. The relationship between concentration level of Co, Cu, and As given by XRF
and ICP-MS after excluding the outliers.
observed from the graph, a significant portion of the data points were randomly scattered
throughout the graph with an even lower R2 value of 0.0248. For a given ICP-MS value (X =
2.0 ppm), the corresponding XRF values ranged from Y = 20 ppm to Y = 50 ppm. There
was no linear regression trend observed for Sb.
42
CHAPTER 5
DISCUSSION
This study mainly uses ratio of concentrations by two measurements, scatter plots,
regression line and R2 value to interpret the relationship and association between using
methods of ICP-MS and XRF for analyzing elemental concentration in house dust. In this
study, the ratio indicates the similarity of concentrations within a certain range given by both
measurements (XRF/ICP-MS). The scatter plot reveals a basic linear relationship between
these two variables for most of the concentration data and the outliers, ID#119-1-01 and
ID#119-2-01, were identified.
Similar ratios of XRF/ICP-MS means the results given by XRF and ICP-MS are
similar. Either high or low ratio of XRF/ICP-MS demonstrates that given a certain element,
the concentration by XRF extends far beyond or below the result from the ICP-MS.
Followed by analysis of the scatter plot and drawing of linear regression, the results can be
categorized into three groups: (1) Similar ratios (ratios within a certain range with the
previous measurement ICP-MS) with distinct linear regression (Pb, Fe, Mn, Zn and Cu); (2)
a higher or lower ratio with distinct linear regression (Co and As); (3) no distinct linear
regression (Ni, Sn, Cr, Sb, Cd and Mo).
An outlier is defined as a data point that emanates from a different model than do the
rest of the data. Outlier detection is important for effective modeling and should be excluded
from such model fitting. In every plot, from Figures 5, 6, 7, 8 and 9, a handful of data points
standed out far away from the rest of the data points, specifically ID# 119-1-01 and ID#1192-01. More specifically, one outlier (ID#119-1-01) was identified for element Pb and Co.
ID#119-1-01 and ID#119-2-01 were identified to be outliers for elements Zn, As, Co and
Mo. ID#119-2-02 was only identified to be an outlier for elements Fe, Mn, Cu, Ni, Sn, and
Cr.
As the complete dataset (with outliers) was included in the plot for linear regression,
the fitted model turned out to be poor, virtually in every plot for every element of interest.
This was paralleled with the results from elemental concentration ratio XRF/ICP-MS. The
43
ratios from these two locations either were extremely high or below the normal range. This
could be a result of sample contamination or mistakes from the procedure.
To determine whether the data presumed to be outlier came from different sources,
the original house dust samples, ID# 119-1-01 and ID# 119-2-01, were weighed again,
followed by three consecutive measurements by XRF for each location to verify the
concentration of 13 elements. No systematic error was identified. No data entry error was
found. The result was shown in Table 4. There was only a slight decrease in concentration
level for each of the 13 elements; no significant difference was observed in general, as
compared to the original data. For example, the concentration level of element Cr still
appeared as high as (673.67 ± 69.67) ppm, similar to the original result (683.33 ± 71) ppm
for ID#119-1-01. ID#119-2-01, which resembles the same manner, had original
concentration levels from (586 ± 66) ppm slightly lower down to (578.33 ± 64) ppm. The
elemental concentration of Cu in ID# 119-1-01 and ID# 119-2-01 in the verification test
peaked as high as (93,021 ± 1233.66) ppm and (87679 ± 1110.33) ppm, respectively, with no
notable decline as compared to (96,152 ± 1277) ppm and (83,163 ± 1102) ppm (original).
Therefore, it can be concluded that it remained difficult to interpret the model associated with
the two sets of data points because of the significant difference they were from the rest set of
data, adversely affecting regression coefficient and as a consequence, leading to poor
prediction of Y variable from X variable. Therefore, the two data points were excluded and
removed from the datasets in the following section in this study for regression analysis, as
shown from Figure 10 to 12. A better fit may be possible.
Figure 10 illustrated a linear regression for concentration levels of four elements: Fe,
Pb, Mn, and Zn between using XRF and ICP-MS. The effect of the outlier showed to be
significant. The resulting fit is linear for all four elements after excluding the two outlying
points. The bulk of the data points distributed closely to the regression line. For the element
Fe, R2 values increased up to 0.8579 as compared to 0.1424 previously obtained before
excluding the outliers. It suggested that about 85.79% of the variation in Fe concentration
given by XRF was explained by the variation given by ICP-MS. Similar conclusions could
be drawn for Pb, Mn, and Zn. The linear regression for each element was highly significant:
R2 = 0.8275, R2 = 0.8041 and R2 = 0.7408, respectively. There was a dramatic increase in the
R2 value for each element of Mn and Zn from 0.2423 and 0.0058 up to 0.8041 and 0.7408,
44
Table 4. Side-by Side Comparison of First and Second Runs for Elemental
Concentration in House Dust at Locations 119-1-01 and 119-2-01 Using XRay Fluorescence
Sample ID# HH 119‐1‐01 1st Run HH 119‐1‐01
2nd Run Pb +/‐ Cu +/‐ Mo +/‐ Cr +/‐ Mn +/‐ Fe +/‐ Sn +/‐ Cd +/‐ Ni +/‐ Co +/‐ Zn +/‐ As +/‐ Sb +/‐ 1560 ± 11 1591.33 ± 11
96152 ± 1277 93021 ± 1233.66
6750 ± 178 5949.33 ± 167
683.33 ± 71 673.67 ± 69.67
1193.33 ± 70 958.33 ± 65
60219 ± 852 58533 ± 822.67
56.67 ± 39 60 ± 38.67
10 ± 21 4 ± 21
16766 ± 254 16411 ± 250.33
2367.33 ± 142 2318 ± 140.67
26162 ± 356 25663 ± 353.67
819.33 ± 15 819.67 ± 15
25 ± 44 28 ± 43.33
HH 119‐2‐01
1st Run HH 119‐2‐01 2nd Run 0.40 ± 10
1.67 ± 10 83163 ± 1102 87679 ± 1110.33 6102.67 ± 169
6505 ± 167.67 586 ± 66
578.33 ± 64 864.67 ± 63
924.67 ± 61.33 55164 ± 775 56931 ± 765.67 78.67 ± 38
40 ± 37 7.67 ± 21
17 ± 20 15816 ± 241 15480 ± 227.67 2094.33 ± 135
2328 ± 133.67 27374 ± 374
24693 ± 325 783.33 ± 15
786 ± 14 26 ± 43
17 ± 41.33 respectively. The higher R2 values demonstrated a strong positive correlation relationship for
each of the elemental concentration of Fe, Pb, Mn, and Zn between using XRF and ICP-MS.
Around 80% of the variation in concentration of Pb, Mn and Zn given by XRF can now be
explained by the variation given by ICP-MS.
Similar XRF/ICP-MS ratios for Pb, Fe, Mn and Zn can be used as a secondary
supporting evidence to explain the significant positive correlation. Except ID#119-1-01 and
ID# 119-2-01, concentration of Pb, Fe, Mn, and Zn given by XRF was similar to that of ICPMS. Paralleled with their distinct linear regression, it is reasonable to conclude that the
regression equation for each of the four elements gave a strong ability to predict the value of
the concentration by ICP-MS with a given XRF value. XRF measurements for the elements
in house dust are accurate.
Figure 11 shows the linear regression trend for element of Co, Cu and As after
removing the outliers. In comparison with Fe, Pb, Mn and Zn, the R2 value of Co (0.5614)
was slightly lower than that of the other four (80%), suggesting about 56.14% of the
variation in Co concentration given by XRF was explainable by variation given by ICP-MS.
Additionally, elements Cu and As had the same R2 value of 0.515. The scatter plot for As
revealed several data with non-detects on X axis given by XRF which could be the result of
45
500
Concentration by X‐ray Fluorescence (ppm)
Ni concentration by X‐ray Fluorescence (ppm)
Ni
y = 3.088x ‐ 34.536
R² = 0.2519
400
300
200
100
0
0
100
200
300
400
Sn
60
50
y = 0.286x + 17.798
R² = 0.1246
40
30
20
10
0
0
500
y = 0.5851x + 17.855
R² = 0.0441
20 40 60 80 100 120
Concentration by ICP/MS (ppm)
Mo
1500
y = ‐2.7779x + 921.86
R² = 0.0004
1000
500
0
0
2
4
6
Concentration by ICP/MS (ppm)
8
20
40
60
Concentration by ICP/MS (ppm)…
80
Sb
60
y = ‐0.5919x + 27.584
R² = 0.025
50
40
30
20
10
0
0
Concentration by X‐ray Fluorescence (ppm)
0
Concentration by X‐ray Fluorescence (ppm)
Concentration by X‐ray Fluorescence (ppm)
Cr
140
120
100
80
60
40
20
0
Fluorescence (ppm)
Concentration by X‐ray Ni concentration by ICP/MS (ppm)
10
20
30
40
50
Concentration by ICP/MS (ppm)
60
Cd
10
y = ‐0.0099x + 5.22
R² = 6E‐05
8
6
4
2
0
0
2
4
6
Concentration by ICP/MS (ppm)
8
Figure 12. The relationship between concentration level of Ni, Sn, Cr, Sb, Mo and
Cd given by XRF and ICP-MS after excluding the outliers.
the actual concentration was below the minimum detection limit of XRF. The concentration
range of As was below 10 ppm, with highest concentration of 8.67 ppm for ID#110-2-01.
This value was much lower compared to other elements, which concentrations ranging more
than 10-fold for Pb, Mn, Cu, Ni, Sn, Cr, Sb and Co; and 100-fold for Fe, Zn and Mo.
Therefore, comparisons between XRF and ICP-MS with a larger range is necessary to
determine whether XRF analysis is accurate enough for element As. In general, the scatters
for Cu exhibited a better fit for its regression line than As. Plus, the ratio of concentration by
XRF over concentration by ICP-MS for As and Cu were within certain range except ID#1191-01 and ID#119-2-01. The ratio for Co was very high. However, elements Co and Cu had
relatively good linear regression, and represented an R2 value in the range of 0.50 ~ 0.60,
except that more data was needed to verify concentration of As. It can be concluded that
there is a moderate correlation between the two variables. Therefore, the knowledge of the
46
concentration of Co and Cu by XRF is helpful in predicting the result by ICP-MS given the
regression equation.
The bulk of the scatters for elements Ni and Cr, after excluding the outlier, distributed
relatively close to the regression line, compared to that of the other elements Sn and Sb. For
element Mo and Cd, the bulk of the data points spreaded out in all directions throughout the
scatter plot, as was shown in Figure 12. In other words, for a given value of X (ICP-MS),
the corresponding value of Y (XRF) spanned over a wide range, vice versa. This drew a
horizontal regression line almost parallel to X axis across the graph, yielding no association.
The positive linear regression trends were relative weak for element Ni, Sn, and Cr.
The slope of the regression line was negative for element Sb regardless of the effects of the
outlier. R2 values for Cr, Sb, Cd and Mo were as low as 0.0441, 0.025, 0.0004 and 6E-5,
respectively. The low R2 values for these elements suggested that their regression equations
should not predict the concentrations of Cr, Sb, Cd and Mo. Based on the regression line,
these elements are not predictable for ICP-MS values.
This lack of predictability can be partially explained by the instability of the
elemental concentration level relative to house dust weight, primarily for Sn, Cr, Sb and Cd,
identified earlier as shown in Figure 3 and 4. Mo showed a stable elemental concentration
level relative to house dust mass, but there was neither distinct positive or negative
association between using XRF and ICP-MS. Therefore, XRF as an analytical instrument
might not be an ideal analytical method when testing element Mo. As far as elements Sn, Cr
and Cd, because there was little linear regression, XRF would not be a useful tool in their
measurements.
Among all the concentrations obtained in this present study, XRF measurement is
valid for 6 elements. These include: Pb, Fe, Mn, Zn, Cu, and Co. Of which, 5 elements- Pb,
Fe, Mn, Zn and Cu concentrations measured by both methods exhibit linear regression with
nearly 1:1 XRF/ICP-MS ratios. Element Co exhibits a linear regression and high and low
XRF/ICP-MS ratios. XRF measurement is invalid for the remaining elements- Ni, Sn, Cr,
Sb, Cd and Mo because of the lack of linear regression. The valid results for 6 elements in
the present study are discussed and compared with other literatures one by one in sequence
below.
47
Pb has been frequently studied. In house dust, Pb shows a wide variation in
individual concentrations. Its levels ranged from 14.00 to 171.67 ppm with XRF/ICP-MS
ratios ranged from 0.83 to 1.64. Only six samples (ID#105-1-01, ID#105-1-02, ID#103-301, ID#103-3-02, ID#105-3-01 and ID#121-3-01) had optimum sample dust mass of 1.62 g.
Pb levels by XRF at these locations were 95.00 ppm, 89.67 ppm, 96.67 ppm, 32.33 ppm,
16.67 ppm and 171.67 ppm, respectively, with XRF/ICP-MS ratios of 1.03, 1.31, 1.06, 1.62,
1.08 and 0.83, suggesting that a general agreement of Pb levels was reached between XRF
and ICP-MS measurement. The mean concentration of Pb in household dust in the study was
significantly higher than the maximum daily load (MDL) which is 0.05 mg/kg (ppm). The
MDL can be explained by the sample origins. All of the samples collected in the present
study come from the living rooms and bedrooms of smoking homes where elevated Pb
concentration is expected (Kalcher, Kern, & Pietsch, 1993; Rickert & Kaiserman, 1994).
Our results are in general agreement with that identified in dust from households in Arizona
which is 58.8 ppm (O’Rourke et al., 2003); lower than that identified in some cities in United
States which remain a reasonably constant mean or median of 300-1000 mg/kg (ppm)
(Roberts et al., 1992; Solomon & Hartford, 1976; Angle, Marcus, Cheng, & McIntire, 1984),
Hong Kong (Tong & Lam, 2000), Wales (Harper, Sullivan, & Quinn, 1987), England
(Thornton, Davies, Watt, & Quinn, 1990), Edinburgh in Scotland (Raab, Laxen, & Fulton,
1987) and in Denmark (Jensen 1992; Willers et al., 1993). This might be due to the
conditions of sample sources. Lead varies as a function of weather conditions, type of fuel,
distance from roads, traffic density, smelters/mining, dustiness (carpet wear), existence of
fireplace, house age and old paint (Morawska & Salthammer, 2003).
Fe levels in house dust were the highest among all other heavy metals, ranging from
4782.00 – 38979.00 ppm. MDL for Fe in house dust is 20 mg/kg (ppm). These levels are
similar to the results reported in other studies. Rasmussen et al. (2001) reported an average
level of 13150 mg/kg (ppm) of Fe in indoor dust from 10 neighborhoods residency across
Ottawa, Canada using ICP-MS with which our results are in general agreement.
Mn levels ranged from 50.33 – 682 ppm with XRF/ICP-MS ranged from 1.11 to 5.36.
The MDL for Mn in house dust is reported as 0.2 mg/kg (ppm) (CCME, 1991).
Concentrations at about six locations which had optimum dust mass of 1.62 g are 136.33
ppm, 151.00 ppm, 114.00 ppm, 88.67 ppm, 157.67 ppm, and 390.00 ppm. These levels are
48
slightly lower than that of other literature. Rasmussen et al. (2001) reported a mean value of
266.5 mg/kg (ppm) for Mn. Similar XRF/ICP-MS ratios showed general agreement of Mn
levels by using two measurements. Sources of Mn in house dust are atmospheric fall out of
petrol, tyre wear, corrosion of metallic parts of consumer products and the release of products
when cooking and burning wood in the fireplace (Oomen, Janssen, & Dusseldorp, 2008).
Zn concentrations were significantly higher than other heavy metals, ranging from
340 – 3448 ppm with XRF/ICP-MS ratios ranged from 0.84 to 2.33. MDL for Zn in house
dust is 1.0 mg/kg (ppm) (CCME, 1991). The six samples (ID#105-1-01, ID#105-1-02,
ID#103-3-01, ID#103-3-02, ID#105-3-01 and ID#121-3-01) which had optimum dust sample
mass of 1.62 g showed Zn levels of 818.33 ppm, 1152.67 ppm, 825.00 ppm, 340.33 ppm,
930.00 ppm and 1466.67 ppm, respectively. Sample 113-1-01 and 113-1-02 showed Zn
concentration 3448.00 ppm and 3265.67 ppm, significantly higher than MDL 1.0 ppm
(CCME, 1991). The levels of Zn in house dust reported by other literature varied from 8001600 mg/kg (ppm) (Fergusson & Kim, 1991; Harrison, 1979). The levels in this study were
in satisfactory agreement with the reported levels range (Davies et al., 1985; Rasmussen et
al., 2001). Zn normally originates from the auto-mobiles such as lubricating oils, tires, metal
plating, soil and ash (Madany et al., 1994), indicating that high levels of Zn in household dust
can be attributed to sources including infiltration of outdoor pollutants or incense burning in
addition to smoking. Additionally, rubber carpet underlays or backings were identified as
another significant source of Zn, with some contribution from galvanized iron roofs (Kim &
Fergusson, 1993; Morawska & Salthammer, 2003).
Cu levels in house dust ranged from 28.33 – 244.33 ppm. MDL for Cu in indoor dust
is 0.50 mg/kg (ppm) (CCME, 1991). Concentrations by XRF at locations with optimum dust
weight of 1.62 g were 94.67 ppm, 101.00 ppm, 95.00 ppm, 47.67 ppm, 91.67 ppm and
134.67 ppm. These levels were slightly lower than those reported by other workers in
Ottawa (Rasmussen et al., 2001) which were 157.30 ppm, 206.08 ppm in North Wales
(Davies et al., 1985), and 170.69 ppm in New Zealand (Kim & Fergusson, 1993). Factors
affecting the presence and concentration of Cu in indoor dust are usually attributed to road
traffic, distance from road, dustiness (carpet wear), metal workers, soil and the existence of
fireplace (Madany & Crump, 1993).
49
Co concentrations by XRF ranged from 45.00 – 388.00 ppm. However, as discussed
earlier, Co measurements by XRF and ICP-MS follow linear regression. The equation y =
71.104x + 5.9299 can be converted as to calculate values by ICP-MS. The converted
equation x = (y – 5.9299)/71.104 yielded Co concentration levels by ICP-MS ranged from
0.55 – 5.37 ppm. MDL for cobalt in house dust is reported as little as 0.10 mg/kg (ppm)
(CCME, 1991). Compared with other literature, Co concentration by ICP-MS was slightly
lower but in general agreement with that reported by Rasmussen et al. (2001). In which,
cobalt was identified as 8.77 mg/kg (ppm).
Comparing our study to other recent studies that used XRF as instrument measuring
elements in settled house dust, Zacco et al. (2009) investigated the heavy metals presence in
settled house dust in the largest province of Brescia, Italy in 2009. They applied XRF to
identify heavy metals in deposited dust samples that were collected in representative
residential households throughout the province. The analysis was able to determine the most
probable metal origins by identifying those with large variations in concentration, reaching
values 20 times larger than minimum percent values, including: Cr, V, Cu, Zn, Ni, Pb, Mn
and Br, and those with low variation in concentration- with difference between maximum
and minimum percent value less than 7 times including: Al, Si, K, Ca and Ti. In our study,
the results that have shown more than 7 times between maximum and minimum values
include: Pb, Mn, Zn, Cu, As, Ni, Sn, Cr and Cd. For Fe, Sb and Mo there was less than 7
times variation between maximum and minimum values. Zacco et al. (2009) had observed a
higher amount of Mn and Fe concentrations found in the Valcamonica area which can be
correlated to the historical presence of Ferro-Mn alloy industry activities. In this study, the
same trend had been identified for Fe concentration (ranged from a minimum of 4782.00
ppm to a maximum of 21855.00 ppm), over 20 times higher than any other elements.
50
CHAPTER 6
CONCLUSIONS
In conclusion, XRF offers excellent concentration analysis for seven elements: Pb,
Fe, Mn, Zn, Cu and Co. There are strong positive linear regressions for each of the element
concentration between using XRF and ICP-MS. R2 values are relatively high (82.75%,
85.79%, 80.41%, 74.08%, 51.5% and 56.14%) for these six elements as compared to the rest
of the other elements. The ratios of XRF/ICP-MS are within a similar range (close to 1:1) for
Pb, Fe, Mn, Zn and Cu; but high or low for element Co. However, because of the strong
positive linear regression for each of the element, the XRF measurement is accurate enough
for Pb, Fe, Mn, Zn, Cu and Co. Each element can use regression equations to predict the
actual levels given the results of XRF.
XRF is not accurate in predicting elemental concentration for Ni, Sn, Cr, Sb, Cd and
Mo. There were neither significant positive nor negative linear regressions observed for
these six elements. R2 values are as low as 25.19% for Ni, 12.46% for Sn, and less than 0.1%
for Cr, Sb, Cd and Mo.
In general, investigators involved with elemental analysis generally have two working
instrument techniques – Wet Chemistry and XRF Spectrometry. Instrument techniques
involved with wet chemistry (such as ICP-MS) are often time-consuming. Sample specimen
is destroyed and often necessary to employ concentrated acids or other hazardous materials.
In comparison, this study used XRF Spectrometry techniques. It has the feature of
easy and quick identification and quantification of elements over a wide dynamic
concentration range, from PPM levels up to virtually 100% by weight. Samples require little
or no preparation prior to analysis. However, it requires relatively larger amount of dust
mass (1.62 g) compared to the use of ICP-MS (200 mg) (Mohammadian, 2010). Results are
obtained within seconds or minutes. Most importantly, it has the advantage of lower cost per
sample than that of ICP-MS per sample. Therefore, this study has demonstrated that XRF is
a good technique for major and trace elements (Pb, Fe, Mn, Zn, Cu, and Co) measurement in
house dust because of the ease and low cost of sample preparation. Once the results of XRF
51
are known, there is no need to use the more expensive instrument such as ICP-MS to test.
Within the linear regression model, the XRF measurement is accurate enough for Pb, Fe, Mn,
Zn, Cu and Co) or equations can be used to predict the accurate concentrations from that of
XRF measurements for the above six elements.
Further studies would include performing the same analysis with an increased sample
size since in this study there were only 24 samples left and available for testing, of which,
only 6 locations have house dust mass optimized for measurements. Another topic of interest
would be to use XRF in other environments to conduct elemental analysis besides at homes
where residents smoke. Other environmental topics could include roadside traffic,
occupational settings and non-smoking homes.
52
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59
APPENDIX
OVERSIZE TABLES
Table 5. Sample Dust Run at House Dust Mass Range from 0.3 g ~ 3.0 g by X-Ray Fluorescence for
Optimization of Dust Mass
60
Table 6. Sample Dust Run at House Dust Mass Range from 1.0 g ~ 2.0 g by X-Ray Fluorescence for Optimization
of Dust Mass
61
* p<0.05
Dust Weight (g) SAMPLE ID#
HH_V101‐1‐01_(1)
HH_V101‐1‐01_(2)
HH_V101‐1‐01_(3)
1.00
AVERAGE
HH_V101‐3‐02_(1)
HH_V101‐3‐02_(2)
HH_V101‐3‐02_(3)
0.69
AVERAGE
HH1_V103‐1‐01_(1)
HH1_V103‐1‐01_(2)
HH1_V103‐1‐01_(3)
1.09
AVERAGE
HH_V103‐1‐02_(1)
HH_V103‐1‐02_(2)
HH_V103‐1‐02_(3)
1.54
AVERAGE
HH_V103‐2‐01_(1)
HH_V103‐2‐01_(2)
HH_V103‐2‐01_(3)
1.08
AVERAGE
HH_V103‐2‐02_(1)
HH_V103‐2‐02_(2)
HH_V103‐2‐02_(3)
1.15
AVERAGE
HH_V103‐3‐01_(1)
HH_V103‐3‐01_(2)
HH_V103‐3‐01_(3)
1.63
AVERAGE
HH_V103‐3‐02_(1)
HH_V103‐3‐02_(2)
HH_V103‐3‐02_(3)
1.62
AVERAGE
HH1_V109‐2‐01_(1)
HH1_V109‐2‐01_(2)
HH1_V109‐2‐01_(3)
0.85
AVERAGE
HH1_V110‐2‐01_(1)
HH1_V110‐2‐01_(2)
HH1_V110‐2‐01_(3)
1.09
AVERAGE
HH1_V113‐1‐01_(1)
HH1_V113‐1‐01_(2)
HH1_V113‐1‐01_(3)
0.67
AVERAGE
HH1_V119‐1‐01_(1)
HH1_V119‐1‐01_(2)
HH1_V119‐1‐01_(3)
1.07
AVERAGE
490.00
475.00
481.00
482.00
526.00
541.00
543.00
536.67
150.00
136.00
142.00
142.67
210.00
187.00
209.00
202.00
122.00
134.00
131.00
129.00
316.00
333.00
337.00
328.67
90.00
93.00
73.00
85.33
262.00
263.00
253.00
259.33
51.00
51.00
51.00
51.00
13.00
11.00
17.00
13.67
87.00
91.00
96.00
91.33
16937.00
16838.00
16524.00
16766.33
Ni
Cr
31.00
33.00
45.00
36.33
42.00
42.00
62.00
48.67
25.00
29.00
14.00
22.67
83.00
72.00
55.00
70.00
48.00
41.00
46.00
45.00
112.00
116.00
99.00
109.00
4.00
3.00
‐3.00
1.33
162.00
140.00
153.00
151.67
13.00
19.00
12.00
14.67
55.00
41.00
67.00
54.33
32.00
41.00
44.00
39.00
713.00
695.00
642.00
683.33
107.00
112.00
105.00
108.00
152.00
148.00
151.00
150.33
92.00
96.00
105.00
97.67
43.00
53.00
55.00
50.33
109.00
112.00
112.00
111.00
82.00
90.00
95.00
89.00
115.00
108.00
119.00
114.00
99.00
83.00
84.00
88.67
350.00
370.00
345.00
355.00
681.00
677.00
689.00
682.33
173.00
169.00
177.00
173.00
1175.00
1209.00
1196.00
1193.33
Mn
Fe
12792.00
12609.00
12721.00
12707.33
14820.00
15033.00
14844.00
14899.00
7799.00
7793.00
7754.00
7782.00
4805.00
4769.00
4772.00
4782.00
9736.00
9749.00
9715.00
9733.33
7468.00
7495.00
7546.00
7503.00
9364.00
9191.00
9200.00
9251.67
7379.00
7318.00
7446.00
7381.00
18715.00
18806.00
18672.00
18731.00
39108.00
38795.00
39007.00
38970.00
11855.00
11867.00
11973.00
11898.33
60218.00
60833.00
59607.00
60219.33
82.00
75.00
80.00
79.00
118.00
116.00
108.00
114.00
129.00
134.00
127.00
130.00
24.00
32.00
29.00
28.33
162.00
163.00
159.00
161.33
39.00
37.00
41.00
39.00
101.00
91.00
93.00
95.00
46.00
47.00
50.00
47.67
183.00
188.00
184.00
185.00
94.00
92.00
95.00
93.67
207.00
214.00
220.00
213.67
96879.00
96843.00
94735.00
96152.33
Cu
682.00
685.00
695.00
687.33
762.00
752.00
744.00
752.67
850.00
842.00
844.00
845.33
231.00
237.00
236.00
234.67
1088.00
1073.00
1082.00
1081.00
382.00
392.00
380.00
384.67
825.00
828.00
822.00
825.00
335.00
341.00
345.00
340.33
1038.00
1026.00
1022.00
1028.67
817.00
804.00
807.00
809.33
3411.00
3457.00
3476.00
3448.00
26554.00
26146.00
25787.00
26162.33
Zn
As
‐1.00
0.00
0.00
‐0.33
‐2.00
1.00
‐1.00
‐0.67
‐2.00
‐3.00
‐3.00
‐2.67
‐2.00
‐3.00
‐1.00
‐2.00
‐1.00
‐1.00
0.00
‐0.67
‐3.00
‐4.00
‐3.00
‐3.33
‐3.00
0.00
1.00
‐0.67
1.00
0.00
0.00
0.33
4.00
1.00
2.00
2.33
9.00
8.00
9.00
8.67
0.00
2.00
3.00
1.67
827.00
818.00
813.00
819.33
1.00
0.00
0.00
0.33
0.00
0.00
0.00
0.00
0.00
‐1.00
0.00
‐0.33
‐1.00
‐1.00
‐1.00
‐1.00
0.00
0.00
0.00
0.00
‐1.00
‐1.00
0.00
‐0.67
‐1.00
‐1.00
0.00
‐0.67
0.00
‐1.00
‐1.00
‐0.67
1.00
0.00
0.00
0.33
0.00
0.00
0.00
0.00
11.00
9.00
10.00
10.00
5304.00
5282.00
5150.00
5245.33
Se
Zr
169.00
166.00
168.00
167.67
202.00
207.00
202.00
203.67
153.00
151.00
153.00
152.33
66.00
67.00
65.00
66.00
202.00
202.00
198.00
200.67
86.00
86.00
83.00
85.00
137.00
137.00
136.00
136.67
78.00
78.00
79.00
78.33
519.00
519.00
520.00
519.33
583.00
576.00
579.00
579.33
512.00
511.00
518.00
513.67
569.00
563.00
556.00
562.67
977.00
997.00
1016.00
996.67
1222.00
1224.00
1180.00
1208.67
1069.00
1061.00
1129.00
1086.33
827.00
812.00
785.00
808.00
1305.00
1255.00
1274.00
1278.00
961.00
975.00
991.00
975.67
916.00
888.00
876.00
893.33
767.00
774.00
818.00
786.33
956.00
959.00
948.00
954.33
581.00
590.00
537.00
569.33
1099.00
1206.00
1254.00
1186.33
6773.00
6863.00
6614.00
6750.00
Mo
Ag
0.00
‐5.00
‐7.00
‐4.00
2.00
5.00
‐2.00
1.67
4.00
‐9.00
2.00
‐1.00
2.00
8.00
2.00
4.00
‐5.00
‐4.00
‐1.00
‐3.33
‐11.00
‐9.00
‐8.00
‐9.33
‐5.00
‐2.00
‐3.00
‐3.33
0.00
‐11.00
‐7.00
‐6.00
‐5.00
‐5.00
‐4.00
‐4.67
‐1.00
‐4.00
9.00
1.33
2.00
0.00
0.00
0.67
52.00
26.00
41.00
39.67
Cd
‐4.00
6.00
‐1.00
0.33
13.00
4.00
7.00
8.00
7.00
‐1.00
7.00
4.33
4.00
‐4.00
‐1.00
‐0.33
‐1.00
7.00
11.00
5.67
2.00
‐4.00
‐8.00
‐3.33
7.00
3.00
9.00
6.33
8.00
4.00
‐4.00
2.67
8.00
‐1.00
5.00
4.00
20.00
0.00
8.00
9.33
8.00
5.00
13.00
8.67
‐18.00
‐12.00
10.00
‐6.67
Sn
26.00
21.00
6.00
17.67
33.00
14.00
17.00
21.33
25.00
21.00
23.00
23.00
12.00
‐10.00
1.00
1.00
18.00
‐3.00
16.00
10.33
26.00
16.00
29.00
23.67
35.00
19.00
31.00
28.33
26.00
11.00
14.00
17.00
26.00
33.00
19.00
26.00
19.00
16.00
0.00
11.67
24.00
29.00
26.00
26.33
65.00
41.00
64.00
56.67
Sb
Table 7. Average Elemental Concentration in House Dust Samples Using X-Ray Fluorescence (ppm)
12.00
22.00
21.00
18.33
35.00
15.00
24.00
24.67
25.00
6.00
13.00
14.67
16.00
31.00
28.00
25.00
23.00
10.00
26.00
19.67
12.00
43.00
21.00
25.33
25.00
41.00
31.00
32.33
36.00
20.00
42.00
32.67
38.00
43.00
66.00
49.00
21.00
14.00
‐8.00
9.00
‐4.00
21.00
36.00
17.67
‐23.00
‐42.00
25.00
‐13.33
‐2.00
0.00
‐3.00
‐1.67
0.00
0.00
‐1.00
‐0.33
3.00
2.00
1.00
2.00
‐3.00
‐3.00
‐3.00
‐3.00
‐1.00
0.00
‐3.00
‐1.33
‐4.00
‐3.00
‐2.00
‐3.00
‐1.00
‐2.00
‐3.00
‐2.00
‐2.00
‐2.00
‐2.00
‐2.00
3.00
7.00
3.00
4.33
4.00
2.00
2.00
2.67
‐2.00
1.00
1.00
0.00
48230.00
47864.00
47081.00
47725.00
Hg
61.00
61.00
62.00
61.33
71.00
74.00
71.00
72.00
32.00
32.00
35.00
33.00
14.00
15.00
13.00
14.00
38.00
38.00
40.00
38.67
21.00
24.00
22.00
22.33
34.00
33.00
30.00
32.33
16.00
17.00
17.00
16.67
90.00
99.00
95.00
94.67
67.00
70.00
73.00
70.00
81.00
80.00
78.00
79.67
‐1631.00
‐1616.00
‐1582.00
‐1609.67
Pb
(table continues)
‐152.00
‐190.00
‐211.00
‐184.33
‐131.00
‐308.00
‐131.00
‐190.00
‐153.00
‐169.00
‐169.00
‐163.67
‐354.00
‐341.00
‐349.00
‐348.00
‐29.00
‐17.00
‐158.00
‐68.00
‐326.00
‐305.00
‐273.00
‐301.33
‐102.00
‐83.00
‐32.00
‐72.33
‐368.00
‐233.00
‐313.00
‐304.67
247.00
244.00
177.00
222.67
728.00
590.00
757.00
691.67
‐52.00
‐125.00
25.00
‐50.67
3585.00
3507.00
3329.00
3473.67
Ba
62
* p<0.05
Dust Weight (g) SAMPLE ID#
HH_V101‐2‐02(1)
HH_V101‐2‐02(2)
HH_V101‐2‐02(3)
0.66 AVERAGE
HH_V105‐1‐02(1)
HH_V105‐1‐02(2)
HH_V105‐1‐02(3)
1.62 AVERAGE
HH_V105‐1‐01(1)
HH_V105‐1‐01(2)
HH_V105‐1‐01(3)
1.63 AVERAGE
HH_V105‐3‐02(1)
HH_V105‐3‐02(2)
HH_V105‐3‐02(3)
1.35 AVERAGE
HH_V105‐2‐02(1)
HH_V105‐2‐02(2)
HH_V105‐2‐02(3)
1.54 AVERAGE
HH_V105‐3‐01(1)
HH_V105‐3‐01(2)
HH_V105‐3‐01(3)
1.62 AVERAGE
HH_V105‐2‐01(1)
HH_V105‐2‐01(2)
HH_V105‐2‐01(3)
0.98 AVERAGE
HH_V109‐2‐02(1)
HH_V109‐2‐02(2)
HH_V109‐2‐02(3)
0.77 AVERAGE
HH_V110‐3‐01(1)
HH_V110‐3‐01(2)
HH_V110‐3‐01(3)
1.40 AVERAGE
HH_V113‐1‐02(1)
HH_V113‐1‐02(2)
HH_V113‐1‐02(3)
0.67 AVERAGE
HH_V119‐2‐01(1)
HH_V119‐2‐01(2)
HH_V119‐2‐01(3)
1.32 AVERAGE
HH_V121‐3‐01(1)
HH_V121‐3‐01(2)
HH_V121‐3‐01(3)
1.62 AVERAGE
2139.00
2169.00
2125.00
2144.33
2183.00
2036.00
2303.00
2174.00
2412.00
2427.00
2420.00
2419.67
2881.00
3038.00
2928.00
2949.00
2439.00
2433.00
2372.00
2414.67
2801.00
2718.00
2906.00
2808.33
3232.00
3296.00
3070.00
3199.33
1734.00
1725.00
1821.00
1760.00
4954.00
4961.00
4892.00
4935.67
2998.00
3067.00
3056.00
3040.33
9863.00
10636.00
10529.00
10342.67
5465.00
5732.00
5470.00
5555.67
Ti
Table 7. (continued)
Cr
41.00
60.00
40.00
47.00
29.00
18.00
31.00
26.00
20.00
3.00
16.00
13.00
71.00
61.00
46.00
59.33
37.00
19.00
34.00
30.00
15.00
25.00
10.00
16.67
‐2.00
21.00
14.00
11.00
30.00
10.00
12.00
17.33
60.00
65.00
65.00
63.33
16.00
26.00
36.00
26.00
623.00
559.00
576.00
586.00
33.00
20.00
40.00
31.00
Mn
157.00
125.00
158.00
146.67
150.00
159.00
144.00
151.00
134.00
139.00
136.00
136.33
222.00
194.00
213.00
209.67
180.00
183.00
185.00
182.67
157.00
174.00
142.00
157.67
186.00
181.00
194.00
187.00
231.00
207.00
201.00
213.00
606.00
584.00
610.00
600.00
168.00
179.00
167.00
171.33
885.00
896.00
813.00
864.67
390.00
391.00
389.00
390.00
Fe
Co
Ni
Cu
Zn
As
Se
Rb
Sr
Zr
Mo
Ag
Cd
Sn
Sb
Ba
Hg
Pb
15088.00
179.00
522.00
119.00
911.00
0.00
1.00
29.00
107.00
206.00 1238.00
9.00
9.00
24.00
40.00
21.00
1.00
68.00
15199.00
138.00
516.00
116.00
916.00
0.00
0.00
29.00
109.00
218.00 1240.00
8.00
‐3.00
45.00
29.00
90.00
2.00
68.00
15276.00
177.00
530.00
116.00
923.00
1.00
0.00
28.00
109.00
220.00 1199.00
0.00
19.00
1.00
13.00
70.00
1.00
67.00
15187.67
164.67
522.67
117.00
916.67
0.33
0.33
28.67
108.33
214.67 1225.67
5.67
8.33
23.33
27.33
60.33
1.33
67.67
11208.00
135.00
56.00
102.00 1159.00
3.00
0.00
23.00
147.00
392.00
753.00
10.00
10.00
13.00
25.00
36.00
2.00
88.00
11228.00
106.00
67.00
97.00 1155.00
1.00
1.00
23.00
151.00
394.00
781.00
5.00
‐5.00
28.00
45.00
35.00
4.00
89.00
11246.00
119.00
62.00
104.00 1144.00
1.00
1.00
23.00
149.00
395.00
730.00
1.00
3.00
47.00
44.00
92.00
4.00
92.00
11227.33
120.00
61.67
101.00 1152.67
1.67
0.67
23.00
149.00
393.67
754.67
5.33
2.67
29.33
38.00
54.33
3.33
89.67
10088.00
97.00
20.00
98.00
820.00
2.00
1.00
27.00
186.00
169.00
836.00
10.00
‐6.00
17.00
28.00
114.00
1.00
96.00
10119.00
122.00
27.00
93.00
822.00
1.00
1.00
26.00
185.00
171.00
870.00
2.00
14.00
36.00
18.00
123.00
5.00
96.00
10142.00
110.00
33.00
93.00
813.00
2.00
0.00
25.00
187.00
170.00
941.00
6.00
6.00
21.00
10.00
123.00
0.00
93.00
10116.33
109.67
26.67
94.67
818.33
1.67
0.67
26.00
186.00
170.00
882.33
6.00
4.67
24.67
18.67
120.00
2.00
95.00
15311.00
168.00
103.00
141.00 2404.00
6.00
1.00
27.00
163.00
464.00
809.00
3.00
‐2.00
16.00
12.00
106.00
3.00
96.00
15433.00
200.00
104.00
131.00 2421.00
4.00
1.00
29.00
165.00
466.00
819.00
5.00
‐3.00
36.00
23.00
25.00
4.00
99.00
15215.00
143.00
104.00
139.00 2413.00
6.00
0.00
27.00
161.00
464.00
748.00
5.00
‐3.00
12.00
52.00
89.00
3.00
97.00
15319.67
170.33
103.67
137.00 2412.67
5.33
0.67
27.67
163.00
464.67
792.00
4.33
‐2.67
21.33
29.00
73.33
3.33
97.33
11622.00
170.00
60.00
137.00 1300.00
5.00
0.00
25.00
163.00
417.00
875.00
10.00
‐7.00
7.00
25.00 ‐102.00
3.00
90.00
11701.00
92.00
67.00
128.00 1299.00
3.00
1.00
25.00
160.00
415.00
882.00
3.00
4.00
8.00
20.00
37.00
1.00
90.00
11553.00
121.00
66.00
128.00 1287.00
5.00
1.00
25.00
160.00
413.00
803.00
7.00
0.00
4.00
1.00
‐6.00
5.00
85.00
11625.33
127.67
64.33
131.00 1295.33
4.33
0.67
25.00
161.00
415.00
853.33
6.67
‐1.00
6.33
15.33
‐23.67
3.00
88.33
11040.00
138.00
31.00
90.00
926.00
0.00
1.00
30.00
167.00
204.00
924.00
8.00
12.00
31.00
23.00
18.00
3.00
99.00
11057.00
144.00
25.00
98.00
930.00
3.00
0.00
32.00
171.00
203.00
945.00
2.00
6.00
19.00
27.00
46.00
3.00
95.00
11087.00
76.00
28.00
87.00
934.00
1.00
1.00
31.00
171.00
202.00
914.00
1.00
5.00
25.00
31.00
91.00
5.00
96.00
11061.33
119.33
28.00
91.67
930.00
1.33
0.67
31.00
169.67
203.00
927.67
3.67
7.67
25.00
27.00
51.67
3.67
96.67
12706.00
118.00
40.00
113.00 1080.00
2.00
0.00
33.00
197.00
195.00 1134.00
6.00
12.00
16.00
20.00
118.00
5.00
116.00
12655.00
106.00
39.00
119.00 1065.00
6.00
1.00
32.00
189.00
193.00 1183.00
2.00
0.00
21.00
33.00
112.00
1.00
113.00
12599.00
127.00
41.00
112.00 1058.00
3.00
1.00
32.00
189.00
196.00 1170.00
3.00
11.00
19.00
34.00
70.00
5.00
116.00
12653.33
117.00
40.00
114.67 1067.67
3.67
0.67
32.33
191.67
194.67 1162.33
3.67
7.67
18.67
29.00
100.00
3.67
115.00
11624.00
115.00
8.00
152.00
601.00
1.00
1.00
30.00
103.00
520.00
913.00
3.00
‐8.00
38.00
23.00
19.00
0.00
72.00
11854.00
82.00
12.00
158.00
598.00
1.00
1.00
30.00
105.00
523.00
865.00
3.00
9.00
16.00
32.00
113.00
1.00
75.00
11827.00
65.00
13.00
157.00
603.00
2.00
0.00
28.00
103.00
519.00
825.00
3.00
‐2.00
25.00
24.00
158.00
1.00
71.00
11768.33
87.33
11.00
155.67
600.67
1.33
0.67
29.33
103.67
520.67
867.67
3.00
‐0.33
26.33
26.33
96.67
0.67
72.67
32897.00
337.00
38.00
76.00
678.00
6.00
1.00
46.00
465.00
575.00
367.00
8.00
5.00
‐4.00
16.00
718.00
1.00
67.00
33025.00
336.00
42.00
86.00
668.00
3.00
1.00
46.00
468.00
574.00
362.00
8.00
6.00
20.00
30.00
556.00
2.00
67.00
33214.00
200.00
40.00
83.00
669.00
5.00
1.00
48.00
474.00
577.00
350.00
7.00
‐2.00
22.00
15.00
673.00
1.00
66.00
33045.33
291.00
40.00
81.67
671.67
4.67
1.00
46.67
469.00
575.33
359.67
7.67
3.00
12.67
20.33
649.00
1.33
66.67
11835.00
82.00
187.00
244.00 3249.00
1.00
15.00
39.00
137.00
510.00 1198.00
0.00
‐2.00
33.00
18.00
26.00
13.00
105.00
11750.00
63.00
190.00
245.00 3264.00
5.00
15.00
41.00
135.00
513.00 1191.00
1.00
5.00
12.00
30.00
195.00
11.00
97.00
11874.00
31.00
205.00
244.00 3284.00
1.00
15.00
40.00
136.00
513.00 1126.00
2.00
15.00
27.00
36.00
16.00
12.00
99.00
11819.67
58.67
194.00
244.33 3265.67
2.33
15.00
40.00
136.00
512.00 1171.67
1.00
6.00
24.00
28.00
79.00
12.00
100.33
55043.00 2040.00 15799.00 82886.00 27285.00
779.00 4791.00
47.00
373.00
578.00 5924.00
26.00
2.00
47.00
33.00 2932.00 46553.00 ‐1486.00
54771.00 2048.00 15648.00 82814.00 27183.00
770.00 4835.00
48.00
370.00
569.00 6079.00
19.00
20.00
104.00
10.00 2554.00 46300.00 ‐1500.00
55679.00 2195.00 16001.00 83790.00 27656.00
801.00 4946.00
46.00
376.00
584.00 6305.00
43.00
1.00
85.00
35.00 2693.00 46833.00 ‐1530.00
55164.33 2094.33 15816.00 83163.33 27374.67
783.33 4857.33
47.00
373.00
577.00 6102.67
29.33
7.67
78.67
26.00 2726.33 46562.00 ‐1505.33
21853.00
262.00
17.00
131.00 1453.00
1.00
1.00
37.00
206.00
358.00
437.00
3.00
‐1.00
63.00
31.00
287.00
5.00
174.00
21853.00
276.00
5.00
140.00 1473.00
2.00
1.00
36.00
206.00
358.00
472.00
4.00
‐3.00
52.00
55.00
172.00
8.00
172.00
21859.00
290.00
7.00
133.00 1474.00
3.00
0.00
37.00
203.00
358.00
389.00
4.00
7.00
51.00
24.00
149.00
8.00
169.00
21855.00
276.00
9.67
134.67 1466.67
2.00
0.67
36.67
205.00
358.00
432.67
3.67
1.00
55.33
36.67
202.67
7.00
171.67
63