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IDENTIFYING STATISTICAL TRENDS FOR ENVIRONMENTAL QUALITY
BASED ON ARCHIVAL CONVENIENCE DATABASES.
ELIA BENITEZ-MARQUEZ
Center for Environmental Resource Management
APPROVED:
___________________________ Patrick L Gurian, Ph.D., Chair
___________________________ Philip Goodell, Ph.D.
___________________________ Jorge Gardea-Torresdey, Ph.D
___________________________ Alfredo Granados, Ph.D.
__________________________ Charles H. Ambler, Ph.D. Dean of the Graduate School
To my loved mom Elia Margarita, in memoriam,
To my loved husband Ricardo.
IDENTIFYING STATISTICAL TRENDS FOR ENVIRONMENTAL QUALITY
BASED ON ARCHIVAL CONVENIENCE DATABASES
by
ELIA BENITEZ-MARQUEZ
DISSERTATION
Presented to the Faculty of the Graduate School of
The University of Texas at El Paso
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
Center for Environmental Resource Management
THE UNIVERSITY OF TEXAS AT EL PASO
December 2005
iv
Acknowledgements
I will thank always the governmental entities of the U.S.A. for giving me the opportunity to study at the University of Texas at El Paso. I thank to my advisor Dr. Patrick L. Gurian who trusted me accepting to direct me through this journey sharing generously his ideas and strategies and teaching me irreplaceable lessons always precise, complete and friendly. In the realization of this work were remarkable contributions the teaching, professional advising and help of Dr. Philip Goodell and Dr. Jorge Gardea-Torresdey who also granted permission to perform the experiments in the laboratories under his direction in the Chemistry Department of U.T.E.P. Also thanks to Dr. Alfredo Granados for his support as committee member. Thank you to Dr. Charles H. Ambler, Dean of the Graduate School for his important, constant and generous support. Thanks to my teacher Dr. John C. Walton who contributed with his professional advice, as director of the doctorate program, and helping me beyond his responsibilities always friendly. Thanks to my husband M.S. Ricardo Vazquez for sharing his life, many ideas and knowledge, and for bearing with me when I am unbearable. Thanks for teaching me their knowledge and support also to Drs: Nicholas Pingitore, Christopher Eastoe, Barry Hibbs, Josef Sobieraj, William Durrer, and Carl Lieb. Thanks to my friends, who helped me in writing, analyzing, and more, particularly to Alberto Barud-Zubillaga, Dr. Jose Peralta, Dr. G. de la Rosa, Ritesh Mariadas, Alfredo Ruiz, Matt Averill and Arturo Woocay. Thanks to my nieces and nephews just for be there. Part of this work was supported by the Grant “Small-scale Spatial Occurrence Trends of Arsenic in the Ground-water Resources of the Paso del Norte Region,” from the Southwest Center for Environmental Research and Policy, Alberto Barud-Zubillaga, PI, Drs. Patrick Gurian and Dirk Schulze Co-PIs, from 6/2003 to 12/2004, and part by Teaching Assistantships in the Physics Department of U.T.E.P. under Dr. Jorge Lopez’ direction. The El Paso Water Utilities shared with us the major part of the databases employed here. The Geological Sciences Department of U.T.E.P under Dr. Diane Doser’s direction, granted permission to use part of the cuttings from its archives. The Junta Municipal de Aguas y Saneamineto allowed sampling and also shared part of its databases. One part of the databases was downloaded from U.S.G.S. web sites. Thanks also to the U.T.E.P. support by means of the: International Students Program specifically to its director Mr. Nicholas Sweig; to the Graduate School, the Library, and the Chemistry Department for diverse and important support to all the students and particularly to me.
v
Abstract
Modern society greatly impacts the environment in complex, threatening, and not completely understood ways and the need for monitoring and assessment of environmental phenomena has become critical. The major objective of this work is to conduct two case studies in using convenience databases with large spatial or temporal extents to inform environmental assessment, in order to identify guidelines for how such databases can be appropriately used in environmental evaluations.
The first case study addresses correlations between environmental quality and diabetes
with state-wide databases. Statistical techniques clarified intriguing correlations between diabetes and air pollution emissions. Calculation of the correlation of diabetes with Toxic Release Inventory (TRI) emissions confirmed the significant association found by previous research. In contrast, a multivariate regression found that state-wide diabetes rates are not significantly associa ted with TRI emissions, indicating that the bivariate correlation between diabetes and air pollution resulted from confounding.
In the second case study, statistical methods are applied to routine ground water
monitoring data. Correlations of arsenic and several anionic constituents in the Hueco Basin are positive and significant (p-values between 0.001 and 0.05), suggesting that competitive desorption from hydroxide solids plays an important role in mobilizing arsenic. To augment the archival information, two cost-effective cuttings experiments were designed and performed to test the local origin and desorption mechanism suggested by the water analyses as factors associated with arsenic contamination in water in the region. Fifteen well cuttings were ana lyzed for arsenic, iron and total organic carbon. Also the cuttings were leached in pH 9 and 10 solutions and the leachates were analyzed for dissolved arsenic. The important role of solid-phase iron in controlling the dissolved arsenic concentration was strongly supported. Significant associations between dissolved arsenic and solid-phase iron (R square 0.71 p-value < 0.01), and significant associations of arsenic leached from the cuttings with dissolved and solid arsenic (R square 0.62 and 0.66, p-value < 0.05 and 0.01) were found. The correlation between arsenic leached and solid iron was positive but not significant. These cuttings results are consistent with competitive desorption of arsenic which was suggested by the statistical analyses of the archival data.
These case studies show both the promise and limitations of archival, convenience
databases. The first case study demonstrates that statewide databases may be too highly aggregated to use for associating environmental exposures with health outcomes but may be useful for understanding the most important factors driving large-scale variations in disease prevalence. The second case study shows the potential of using archival data in hypothesis development which can serve to target future data collection efforts.
vi
Table of Contents
Page
Acknowledgements....................................................................................................................iv Abstract ........................................................................................................................................ v Chapter 1. Implementing Environmental Assessments by Statistical Analyses ..........1 Chapter 2. Understanding the Associations between Statewide Diabetes.....................3 Prevalence and Air Pollution Emissions ...............................................................................3
1. Diabetes and Air pollution............................................................................................. 3 2. Research Design.............................................................................................................. 4 3. Results.............................................................................................................................. 5 4. Conclusions on Diabetes and TRI Correlations ......................................................... 7
Chapter 3. Arsenic in the Groundwater of the Paso Del Norte Region........................9
1. Arsenic in Drinking Water............................................................................................ 9 2. Locations and Hydrology ............................................................................................. 11 3 Arsenic Geochemistry ................................................................................................... 16 4. Information Sources of Water data ............................................................................ 21 5. Sample Collection......................................................................................................... 23 6. Results............................................................................................................................ 24 7. Discussion of Archival Analyses................................................................................. 42
Chapter 4. Augmenting the Archival Data: Cuttings and Leaching Experiments. ..45
1. Arsenic Desorption in Groundwater. ......................................................................... 45 2. Cuttings Experiment .................................................................................................... 46 3. Leaching from Cuttings Experiment .......................................................................... 47 4. Methodology.................................................................................................................. 48 5. Results of the Cuttings Experiments........................................................................... 52 6. Conclusions ................................................................................................................... 63
Chapter 5. Final Conclusions................................................................................................65 References ..................................................................................................................................68 Appendix A. Sampled Data of Juarez City and Suburbs ................................................71 Appendix B. Detailed information of EPWU wells # 9, 305 and 306...........................73 Curriculum Vitae......................................................................................................................76
1
Chapter 1.
Implementing Environmental Assessments by Statistical Analyses
Because of the complexity of environmental processes, and the accelerated pace with which
modern technology impacts the environment, it is necessary constantly to monitor and assess
environmental quality. The development of these assessments requires large databases with good
spatial and temporal coverage.
Government entities have compiled large amounts of data for different purposes, such as: for
taxation, for monitoring compliance with existing environmental regulations, for military
purposes and for organization. Indeed, the words state and statistics share the same root (Waugh
1952). Information on environmental quality is routinely collected by governments for regulatory
purposes. These databases may provide a valuable source of information with the broad spatial
and temporal scope needed to inform environmental quality assessments. The major objective of
this work is to apply statistical methods in the exploration and modeling of two environmental
problems, using archival governmental databases relating to air and water quality.
Air quality and its connection with diabetes occurrence is the first study case. Two sources of
data are available. The Environment Protection Agency EPA publishes the total pollutant
emissions to the environment by state in the Toxic Release Inventory (TRI) web site.
Information on diabetes occurrence by state is available in the Behavioral Risk Factor
2
Surveillance System (BRFSS) at http://www.cdc.gov/brfss/. Previous researchers have debated
whether the information in these two databases can be combined to assess the potential for
pollution to lead to high diabetes rates (Lockwood 2002, Nicolich 2002). This study will clarify
the usefulness of statewide data and identify the source of the significant correlation between
TRI emissions and diabetes occurrence reported by Lockwood (2002).
The second case study identifies the possible origin and factors that cause the high arsenic
concentrations in the ground water in the region of El Paso County by statistical analysis of
about 6400 observations from 356 wells from 60 feet depth to 1300 feet depth, since 1927.
Additional data will be collected at particular wells. Because the new Maximum Contamination
Level (MCL) for arsenic content in drinking water (10 parts per billion) will be enforced by
January 2006, this study is relevant to El Paso region where 20% of the wells exceed the new
MCL.
These contrasting case studies (one national in scope, addressing air quality; the other regional in
scope, addressing water quality) are intended to serve as guidelines for using archival
convenience databases while at the same time making contributions to the specific cases
considered.
3
Chapter 2.
Understanding the Associations between Statewide Diabetes
Prevalence and Air Pollution Emissions
1. Diabetes and Air pollution
Diabetes is a metabolic disease that prevents human cells from using energy from the glucose
either by inhibiting production of insulin or by disrupting the insulin function. Diabetes is a
leading cause of death in the USA (Parker, 2002). Approximately 3% of the world population
and 6% of the American population has diabetes (Power 2003, ADA 2003). About 19% of
deaths in USA were diabetics in 1999 and the risk of death for diabetic people is twice that for
people who do not have diabetes (National Institute of Diabetes and Digestive and Kidney
Diseases NIDDK, NIH 2003). The effect that environmental pollution has on endocrine
disruptors is an important branch of research and the link between diabetes and environmental
contamination is under investigation by several groups (Henriksen 1987, Roegner 1987, Parker
2002).
In a recent letter to Diabetes Care, Lockwood (2002) presented a statistically significant
correlation between statewide diabetes prevalence reported in the Behavioral Risk Factor
Surveillance System (BRFSS at http://www.cdc.gov/brfss/) for the year 2000, and statewide total
air pollution emissions reported in EPA’s toxic release inventory (TRI 1999) database (r = 0.54,
4
P < 0.0001). Lockwood noted that a correlation does not necessarily result from a causal
relationship but called for attention to it. In response, Nicolich (2002) criticized Lockwood’s use
of statewide data to show causal relationships, Nicolich presented four highly statistically
significant correlations between statewide diabetes prevalence and factors that would not be
expected to be causal factors in diabetes: latitude of the state capital, longitude of the state
capital, state population (R = 0.46, p-value = 0.001), and numerical position of the state name on
an alphabetized list (R_square = 0.48, p-value = 0.001). Nicolich stated that relationships should
be based on individual- level data, rather than statewide data, and on the existence of a plausible
mechanism.
This study investigated the puzzling calculations presented by Lockwood and Nicolich. This
analysis is intended to serve as a case-study to evaluate the utility of state-wide data for use in
understanding associations between environmental exposures and health outcomes.
2. Research Design A multivariate regression model was used to examine the influence of a variety of potential
explanatory variables on diabetes prevalence while controlling for partial confounding between
dependent variables.
The correlations calculated by the authors were repeated. Lockwoods’ results were found
precisely as published. For Nicolich’s only one could be closely duplicated: the correlation
between diabetes and the total population by state. Nicolich obtained R = 0.46, p-value = 0.001,
5
while in this work after logarithmic transformations of both variables we obtained R = 0.48, p-
value = 0.001. Nicolich’s correlation between diabetes and alphabetic rank was R = 0.49, P-
value < 0.001, while we found R = - 0.017, P-value = 0.904
Statewide diabetes prevalence was regressed on both state population and TRI emissions, as
these factors had been shown to be significant in the bivariate analysis. In addition, the
proportions of the state population in each of three ethnic groups (African American, Latino, and
White) were included in the regression, because ethnicity is known to influence diabetes
prevalence. All variables were log-transformed as this was observed to produce roughly
normally distributed residuals.
3. Results
The results of this study case have been published in the journal Diabetes Care
(Benitez-Marquez, Diaz and Gurian 2004).
The results (Table 1) indicate that only the association between statewide diabetes prevalence
and proportion of African American population is statistically significant. The bivariate
correlations noted by Lockwood and Nicolich appear to result from partial confounding with this
factor. African Americans have historically migrated to large, indus trial states, such as New
York, Michigan, Louisiana, and Texas, that would be expected to have both high populations and
high TRI air emissions. In contrast, rural northern states such as Vermont, North Dakota, Idaho,
6
have low populations, low TRI emissions, and low proportions of African Americans. The
negative correlations with latitude and longitude reported by Nicolich appear to result from
higher African American populations in the southeastern states.
This does not rule out air pollution as a causal factor in diabetes. However, the analysis of state-
level emissions data is unlikely to yield much insight into this issue given the lack of
contaminant-specific exposure information, the small variation in the statewide prevalence that
would be expected from environmental factors, and the many potentially confounding factors.
Further research into the causes of diabetes is certainly desirable (Lockwood 2002), and
important researches include individual- level studies and mechanistic studies (Calvert 1999,
Michalek 1999, Henriksen 1987, Roegner 1987, Parker 2002).
Table 1. Linear regression coefficients in a multivariate linear regression. The natural logarithm of the state prevalence of diabetes is the dependent variable.
Variable Standardized
coefficient
t
value Significance
Log TRI Emissions 0.235 1.3 0.201
Log Total Population 0.079 0.39 0.701
Log %White -0.125 -0.92 0.363
Log% African-American 0.487 3.42 0.001
Log %Latin -0.142 -0.94 0.352
7
Figure 1 shows a P-P plot for the residuals of the multiple linear regression of log transformation
of diabetes occurrence, compared to a normal distribution (straight line). It shows a good fit for
the normal distribution.
P-P Plot of Log Diabetes
Regression Std Residuals
Observed Cum Prob
1.00.75.50.250.00
Exp
ecte
d C
um P
rob
1.00
.75
.50
.25
0.00
Figure 1. Normal P-P plot for the regression standard residuals with Logarithm of
Diabetes occurrence as dependent variable, compared to Normal distribution (straight line).
4. Conclusions on Diabetes and TRI Correlations
The findings for this case can be summarized as follows:
• With multivariate analysis, the TRI air emissions appear to be partially confounded with
African-American population at statewide level, this partial confounding explains the
puzzling statistical link between TRI emissions and diabetes.
8
• Statewide data is important to prioritize between regions but not for identifying causal
factors.
• This highlights importance of ethnicity as a risk factor and may lead to some health
policies in the highly industrialized (high TRI) states targeting Afro-American population
for diabetes prevention programs.
9
Chapter 3.
Arsenic in the Groundwater of the Paso Del Norte Region
1. Arsenic in Drinking Water High arsenic concentrations in drinking water have been linked to diverse types of cancer and to
other serious diseases (Berg 2001, Siegel 2002). Arsenic has been associated with cancer since
1879 when miners in Saxony Germany presented high lung cancer rates (Smith 2002).
Epidemiological studies in Argentina Taiwan, Chile, Japan, Mexico and Bangladesh have
associated high arsenic in drinking water with skin, kidney, lung and bladder cancers (e.g.
Selinus et al. 2005, Smedley and Kinniburg 2002, Smith 2002). In 1999, the National Research
Council estimated that 50 ppb of arsenic in drinking water could present the highest risk of
known contaminants in water, more than 50 times higher than any other contaminant regulated in
the US. To reduce this health hazard, in 2001 the U.S. Environmental Protection Agency (EPA
2001) lowered the Maximum Contaminant Level (MCL) for arsenic in drinking water from 50 to
10 micrograms per liter (µg/l) or, equivalently, from 50 to 10 parts per billion (ppb). The new
MCL will be enforced by January 2006.
The objective of this study is to identify possible causes of the high arsenic concentrations in the
ground water of El Paso County by statistical analysis of about 6400 archival data from over 350
wells during the last 70 years. A limited amount of additional data was collected where necessary
10
to fill gaps in the archival data set. This study is important given that in the El Paso region more
than 20% of the drinking wells exceed the new MCL. This case is also intended to serve as a test
bed for the development of guidelines for using archival convenience databases, while at the
same time making contributions to understanding the arsenic mobilization mechanisms in the
study area.
Only limited archival information about the solid phase of the aquifers and the content of arsenic
in the basin fill was available previous to this work. However, McCutcheon (1982) analyzed the
rock content in the Franklin Mountains, finding arsenic concentrations from 18 to 280 ppm.
Content of arsenic in rocks ranges from 1 ppm up to 18 ppm (Selinus et. al. 2005), and the
Earth’s crust has a broad average of 2 ppm (Siegel 2002, Smedley and Kinniburg 2002).
To establish the local or foreign source of arsenic in the solids of the aquifer, a cuttings
experiment was designed, finding that arsenic exists in the sediments in sufficient amounts as to
control the concentration of arsenic in the water at least partially. In relation to the specific
mechanism that mobilizes the arsenic from the solids of the aquifer into the groundwater, a
second, leaching experiment was designed also as part of this work. Both experiments are
discussed in the next chapter of this document, as they complement and extend the possible
conclusions from the archival information.
11
2. Locations and Hydrology The Paso Del Norte Region is an urbanized region of the Rio Grande Valley located at the
intersection of the U.S. states of Texas and New Mexico and the Mexican state of Chihuahua
(Figures 1a, b). The region includes the cities of El Paso, Texas, and Ciudad Juarez, Mexico. It is
located in the northern Chihuahuan Desert and has a subtropical arid climate (Fisher and
Mullican1990). Rainfall averaged 7.8 inches and temperature 63.4 °F (from – 8 °F to 109 °F)
during the period from 1960 to 1980 (Fisher and Mullican1990). The Rio Grande River and the
Franklin Mountains characterize geographically the region. The Rio Grande River serves as the
natural border between the U.S. and Mexico. There are two basins in the region, the Hueco
Basin, which is located east of the Franklin Mountains and the Mesilla Basin, which is located
west of the Franklin Mountains. These basins are filled with Tertiary and Quaternary alluvial
unconsolidated sediments, are not hydraulically connected. Until relatively recently, both
aquifers drained to the Rio Grande River. However during the last 30 years, water withdrawals to
serve the growing population of the region lowered the potentiometric levels in the basins below
the level of the river. El Paso and Ciudad Juarez are located directly across from each other on
opposite sides of the U.S.-Mexico border. This study case will focus in the drinking water supply
to these two cities whose total population exceeds two million people. The Red Bluff granite
formations in the Franklin Mountains contain up to 281 ppm of arsenic, the Castner formations
up to 64 ppm and the Lanoria formations average 18 ppm (Mc Cutcheon 1982), which exceed
the Earth’s Crust average of 2 ppm (Smedley and Kinniburg 2002) and suggest the material
eroded from the mountains as an important source of the solid phase arsenic in the region.
12
The three main drinking water sources of El Paso are the Hueco Basin aquifer (which provides
30% of the annual water supply for El Paso), the Mesilla Basin aquifer (20% of annual supply),
and the Rio Grande river (50% of annual supply) (EPWU 2002). Ciudad Juarez relies on the
Hueco Basin for 100% of its drinking water supply. The Hueco Basin aquifer underlies both
cities and is crossed by the Rio Grande River from northwest to southeast.
The Franklin Mountains, the Sierra de Juarez, Organ and Hueco Mountains and some other hills
and plutons rise over the region and their erosion over the last 45 million years has filled the
Basins. The Franklin Mountains include rocks belonging to the Castner, Red Bluff and Lanoria
formations that have arsenic concentrations between 18 and 280 ppm (McCutcheon 1982).
The material that fills the Mesilla Basin is from the Quaternary-Tertiary ages (middle Pleistocene
and Oligocene epochs) and consists of unconsolidated alluvium from both nearby mountains and
distant sources outside the basin. The Mesilla basin is neither homogeneous in composition nor
in hydrologic parameters (EPA 1997). The Mesilla Basin includes three hydrologic units, known
as the Santa Fe shallow, the Santa Fe intermediate, and the Santa Fe deep aquifers (Baxfield
2001, EPWU 2003). The shallow aquifer is unconfined, while the intermediate and deep aquifers
are confined aquifers. These three aquifers are separated by discontinuous clay aquitards (EPA
1997, EPWU 2004) that allow for leakage from the shallow into the intermediate, and from this
into the deep aquifer. The two principal mechanisms of recharge to the Mesilla are seepage from
the Rio Grande and deep percolation of irrigation water (EPA 1997). Twenty five out of 58 wells
(43%) in the Mesilla Basin are hydraulically connected with the Rio Grande River. Rock and
mineral sources in the Mesilla Basin include Precambrian granite and metamorphic rocks,
13
Paleozoic carbonate rocks, Tertiary and Quaternary mafic and intermediate volcanic rocks and
intrusions, Tertiary silicic volcanic and plutonic rocks and Quaternary eolian dust. Gypsum is
associated with Precambrian units while pyrite occurs as an accessory mineral in many rock units
(WRRI 2004).
The material that fills the Hueco Basin belongs to the Fort Hancock and Camp Rice formations
of Quaternary-Tertiary ages. The Hueco Basin is an unconfined aquifer for most of its volume.
However, it contains at least one confined unit underlying the Rio Grande between 300 and 700
feet (perhaps deeper) depth. Based on EPWU archives, this aquifer starts under the southern tip
of the Franklin Mountains and continues downstream under the river (perhaps in discontinuous
segments) for at least 20 km and perhaps substantially farther (there are no EPWU records for
wells farther downstream). No explicit characterization of this confined unit was found in the
literature reviewed.
14
Figure 1a. Paso del Norte region including southwestern Texas, southern New Mexico and northern
Chihuahua, Mexico. http://www.nationalatlas.gov
15
Figure 1b. Remote sensing image of the Paso del Norte region.
http://paces.geo.utep.edu/elpjuarez/elpjuarez.html
Some limited recharge in the western portion of the Hueco Basin is due to infiltration of
precipitation and runoff from the Franklin Mountains, and continuous underground inflow comes
from the Tularosa Basin, which is hydraulically connected to the Hueco Basin (Anderholm and
Heywood 2003, EPA 1997).
16
3 Arsenic Geochemistry A review of the scientific literature was conducted to identify physical and chemical processes
that may mobilize arsenic in ground water. Descriptions of these mechanisms are provided
below.
3.1 Competitive Desorption
Arsenic in groundwater is believed to be controlled primarily by interactions between aqueous
and solid phase aquifer materials (Heinrichs and Udluft 1999, Robertson 1989, Stollenwerk
2003, Sracek et al. 2004). Stollenwerk’s review (2003) found that for the common geochemical
conditions in aquifers, most of the As-bearing minerals rarely exist far from the mineralized
zones, and that solubilities of those minerals are much higher than the actual concentrations in
most of the groundwaters. Stollenwerk concluded that desorption of arsenic from the aquifer
solid surfaces is the predominant control on dissolved arsenic concentrations in many
groundwater systems. The adsorption/desorption processes are dependent on: pH, Eh, the
arsenic species, the solid surface properties, arsenic concentration, and the competing ions
concentrations (Robertson 1989, Smedley and Kinninburg 2002, Stollenwerk 2003).
The adsorption processes between the surfaces of the rocks or sediments, and the ions dissolved
in the water are of two main types. When this attraction is due to electrostatic forces between the
ion and a charged surface, thru a oxygen atoms or water molecules layer the process is called
“outer-sphere surface complexation” or “non-specific adsorption” or “physi-sorption” (e.g. Cl, I,
Br, Na, NO3-, CO3
-2, ClO4-). When the attraction forms a chemical bound between the surface-
17
metal and the aqueous- ion, it is stronger and the process is called “inner sphere complexation”,
“specific adsorption” or “chemisorption” (e.g. As, F, Cu, Pb, PO4-3, AsO4
-3) (Foster 2003,
Stollenwerk 2003).
Some anions are known to compete with the arsenic molecules for sorption sites on the aquifer
solids. If an anion displaces an arsenic molecule, the arsenic will desorb from the solid phase
passing into the aqueous phase, increasing the dissolved arsenic concentration. The molecules
that usually occur in groundwater and compete with arsenic oxide (with different strengths) are:
SO4-2, CO3
-2, SiO2, PO4–3, OH- and F-. These species may occur in concentrations orders of
magnitude higher than typical arsenic concentrations, thereby producing a considerable
competitive effect (Holm 2002, Sracek et al. 2004).
Equilibrium chemistry models show a sharp increase in the amount of arsenic(V) desorbed from
iron hydroxides for pH >= 8 (Montoya and Gurian 2004). Experimental evidence (Foster 2003,
Stollenwerk 2003) confirmed that arsenic complexation regularly happens by inner-sphere
surface mechanisms which are relatively independent of the solution ionic strength (I) but
strongly dependent on the solution pH with the arsenic (V) desorption increasing with higher pH.
For example the equations for the substitution of AsO4-3 by CO3
2- molecule in the hydroxide
mineral (=FeOH) are (Montoya and Gurian 2004):
=FeOH + AsO4-3 + 3H+ = =FeH2AsO4 + H2O ….. .(equation 1)
=FeOH + CO3 -2 + 2H+ = =FeHCO3 + H2O ……(equation 2)
18
When carbonate increases in equation 2, the reaction shifts toward the right and the hydroxide
=FeOH concentration diminishes. Then the reaction equation 1 shifts toward the left side,
releasing arsenic into the water. Thus high carbonate concentrations produce high arsenic
concentrations, which imply a positive correlation coefficient between the arsenic and the
carbonate molecules.
3.2 Reductive Dissolution
When iron or manganese are reduced they become more soluble and may release arsenic ions
adsorbed to them as shown by the following summary reactions:
Fe(III) less soluble à Fe(II) more soluble
As(V) more adsorbed à As(III) less strongly sorbed
These reactions increase the concentrations of both metals (Fe and metalloid As) in solution.
Thus a positive correlation between arsenic and iron or manganese would support this
mechanism. Reduced environments generally occur deep in insolated aquifers or when
microorganisms promote the reduction. Dissolved oxygen (DO) is expected to be very low or
absent, and oxidized species are expected to be scarce (Sracek et. al. 2004)
In Bangladesh and West Bengal, reductive dissolution has been found to be the principal process
mobilizing arsenic from the river delta material into the water after organic decomposition
19
(Smedley and Kinniburg 2002, Sracek et al. 2004). Organic material buried for thousands of
years may give rise to this process, and such organic material may be present in sediments
deposited by the Rio Grande.
3.3 Evaporative Concentration
The evaporation of water either during the recharge process or on the surfaces of ancestral lakes,
now buried inside the basins, concentrated the ions, including arsenic and other dissolved solids
in the water (Smedley and Kinniburg 2002, Baxfield 2001). Evaporation also raises the pH as it
increases alkalinity by concentrating CaCO3 and CO3-2
(or HCO3- depending on pH, which in
turn promotes the competitive desorption of arsenic from iron hydroxides (higher pH implies less
positively charged solid surfaces which then adsorb less strongly the negative arsenic(V)
molecules) (Montoya and Gurian 2003, Sracek 2004). The wells studied here are all located in
the saturated zone. Thus evaporation is not a currently active mechanism, but evaporation during
previous geologic time periods or during the recharge process may have created conditions
leading to arsenic concentration and mobilization.
3.4 Upwelling from deeper waters
Excessive pumping or thermal processes may cause deep waters to flow upward to the wells. The
minerals and dissolved ions will be different from those usually found in that region of the
20
aquifer. For the Hueco (EPWU 2003, EPWU 2004) and many other aquifers (see Baxfield for a
number of case studies in New Mexico), deeper waters are usually more mineralized waters.
Such mineralized waters may have high alkalinity and high pH that promotes the arsenic
desorption.
High arsenic concentrations (from 100 to 50,000 ppb) have been reported in geothermal systems
(active or fossil systems, hot springs or well fluids), either on tectonic plate boundaries, in local
hot spots or in rift zones (e.g. the Pacific Plate edge, Yellowstone National Park, the Rio Grande
Rift) (Webster and Nordstrom 2003). Experimental work indicates that the source of arsenic in
geothermal fluids is mainly from the leaching of host rocks (Webster and Nordstrom 2003).
Concentrations of chloride, bromide and fluoride are high and a positive correlation between
arsenic and chloride has been confirmed in most geothermal fields. Sulfide may be low (in
neutral pH, chloride springs) or high (in acid sulfate and bicarbonate springs), and temperature
gradients range from few to hundreds of degrees above ambient. Webster and Nordstrom (2003)
also found that geothermal fluids are commonly derived from meteoric waters and not from
magmatic or volcanic ones.
In western Texas, Hoffer (1977) found geothermal waters in several areas, including El Paso
County. Temperature was measured in 8 samples from the El Paso region and the mean
temperature found to be 37oC (individual samples ranged from 30oC, a threshold value for
geothermal influence, to 58oC). Samples were collected in both the Hueco and Mesilla basins,
including four samples from test wells close to the EPWU Canutillo well field in the Mesilla
Basin included in this work. Arsenic analyses were not conducted on these samples.
21
3.5 Anthropogenic sources
There are four possible anthropogenic sources of arsenic in the region: the ASARCO
smelter (Cu and Pb ores) on the Mesilla basin, the cooper and the petrochemical
refineries both in East town El Paso in the Hueco, and agriculture in the Canutillo area
of the Mesilla.
At Canutillo, Canutillo the shallow wells are low in arsenic, all the wells with higher
arsenic pump water from the Santa Fe intermediate and deep aquifers (between 250 and
900 feet), which is opposite to the expected effect of agricultural pollution into surface
and shallow groundwaters. The idea that ASARCO and the two refineries actively may
cause arsenic pollution, needs to be addressed.
4. Information Sources of Water data
This study used a robust database provided by the El Paso Water Utilities (EPWU) which
included information on the concentrations of 14 different major ions in 287 drinking and non-
drinking water wells (229 in the Hueco and 58 in the Mesilla) and arsenic observations in 199
wells. For Ciudad Juarez data on concentrations of major ions were obtained from the archives of
the Junta Municipal de Aguas y Saneamiento (JMAS). The JMAS archives did not include
information on arsenic concentrations. A data set from the Texas Water Development Bureau of
about 250 drinking and non-drinking water wells was also used. Because of the different nature
of these databases and some degree of overlap between the EPWU and the TWDB databases, the
databases were analyzed separately rather than being pooled.
22
The EPWU archives included 6400 observations for 356 wells from 60 feet depth to 1300 feet
depth, since 1927. From those, about 850 total observations contained arsenic measurements for
about 20 years (1984 to 2003). In the EPWU database, each well has between 1 and 70
observations from different dates and depths (about 50 wells have more than 40 observations
each). For this work, constituent concentrations were averaged by well.
Neither the El Paso nor the Juarez city archives include dissolved oxygen information. The
EPWU archives include a small set of speciation and temperature ana lyses consisting of 40
samples from 7 wells in the Canutillo fields in the intermediate aquifer of the Mesilla Basin
(Wells 301, 303, 305, 306, 307, 308 and 309).
Water from 99 wells was sampled for arsenic as part of this work. Because a great deal of
archival data on the U.S. side of the border was available from the EPWU, only 30 additional
samples were collected from the U.S. (22 from the Hueco and 8 from the Mesilla), mainly to
validate the archival information. However for Ciudad Juarez, no archival information was found
for arsenic, and the bulk of the additional ground water sampling (69 samples) was conducted in
the portion of the Hueco Basin underlying Ciudad Juarez. From these groundwater samples a set
of 9 samples from the Juarez area was also analyzed in the field for dissolved oxygen (DO), pH,
electrical conductivity (EC) and temperature.
Data processing
23
The data was grouped and processed separately for each basin, and all of the observations in
each well were averaged to give one value per well. The averages, standard deviation, ranges,
and the Spearman’s non-parametric (rank-order) correlation coefficients were calculated between
arsenic and the ions in the water. Pearson’s (parametric) correlation coefficients are not used
here since they are based on the raw data, rather than the rank order of the observed values, and
tend to be overly influenced by occasional, very high observations (outliers), such as can be
produced by experimental error or other anomalous processes (well casings rusting, etc.). The
software package used to perform all the statistical analyses and scatter plots was SPSS 11.0.1
Standard version for Windows © 2001. The package used to generate the maps and GIS layers
was ArcView GIS 3.3 © 2002.
5. Sample Collection
Groundwater samples were collected from November 2003 to December 2004 and analyzed for
arsenic by a contract laboratory (the NMSU SWAT laboratory) using ICP EPA method 200.8
(EPA-600/4-91-010 1991). The water samples were collected when the wells were operating and
were drawn at the wellhead, before chlorination. The water was allowed to flow for about three
minutes before sample collection. Samples were collected in 200 ml plastic containers, which
were thoroughly rinsed and filled without headspace. For a small set of samples collected in
Ciudad Juarez, the pH, temperature, and electrical conductivity were measured in the field. The
samples were analyzed within 10 days of the collection date.
24
6. Results
6.1 Statistical analyses of the archival data set
Arsenic concentrations in well water in El Paso are illustrated in Figure 2a. The largest red dots
symbolize arsenic from 16 ppb to 32 ppb, blue 10 ppb to 16 ppb, purple 8 ppb to 10 ppb, aqua 5
ppb to 8 ppb, and pink < 5 ppb. Each value for arsenic is the average of the observations in each
well from 1984 to 2002. The highest arsenic observations are found west of the Franklin Mtns in
the intermediate and deep wells of the Canutillo field within the Mesilla Basin. Most of the
shallow wells there (small dots overlapped with larger dots) have low arsenic concentrations.
Arsenic in the Mesilla is statistically significantly higher than arsenic in EPWU wells in the
Hueco Basin (0.05 significance T-test). A cluster of high arsenic wells in the Hueco is located in
an area beginning near the Airport and extending south under the river, where a series of blue
and red dots show higher than 10 ppb arsenic concentrations. Most of these wells are in areas of
the basin with low potentiometric levels (groundwater head). Figure 2b shows the arsenic
concentrations as dots overlapped with pH concentrations shown as triangles, in the same wells.
Average concentrations of the major ions in aquifers in a macro-region that includes El Paso are
shown in Table 1. The summary values presented are only to compare these aquifers in a general
manner and to give an idea about orders of magnitude. This table considers basins northwest
(New Mexico) and east (Trans-Pecos) of El Paso, the Rio Grande River, and the Hueco and
Mesilla basins.
25
Based on average values it appears that arsenic in the river is low (median 6.1 ppb but increases
after the wastewater discharge up to 10.1 ppb). Sulfate and TDS are much higher in the river
than in the groundwater of the region; bicarbonate and calcium are slightly higher.
The macro-flow direction goes from northwest toward southeast (EPA 1997, Anderholm and
Heywood 2002). Groundwater flowing through the macro-region (constituted by all the aquifers
in Table 1) tends to increase in sodium, potassium, chloride, and in general dissolved solids. A
comparison of the thermal and non-thermal waters in Table 1 for the Trans-Pecos aquifers
indicates that the geothermal waters have higher dissolved solids, sodium, potassium and
chloride and lower concentrations of calcium and magnesium. Geothermal waters are often
relatively deep, older waters that may have had more opportunities for cation-exchange reactions
to occur.
Table 1. Major ions and arsenic concentrations in the Hueco and the Mesilla basins compared to neighbor aquifers Northwest and Southeast of El Paso (aggregated by well). The units are parts per million (ppm) (except As: ppb)
Aquifer Ca Mg Na K Cl SO4 HCO3 SIO2 TDS pH As Hueco Basin1 47 15 152 9 206 90 147 31 640 8.0 7.6 Mesilla Basin1 42 7 167 4.3 115 195 132 31 652 8.4 12
New Mexico Gyp2 636 43 17 NA 24 1570 143 29 2480 NA 183 New Mexico Rhy4 6.5 1.1 38 2 17 15 77 103 222 NA 113 Trans-Pecos TX7
non-thermal waters 125 33 195 8.3 242 NA NA 18 1338 NA NA
Trans-Pecos TX7 thermal waters6 71.6 20.1 502 31.9 663 NA NA 23.8 2635 7.4 NA
Rio Grande6
At El Paso TX 60 11 137 NA 135 4045 190 NA 14345 7.9 6.85
1 EPWU archives drinking water supply wells 2 Eby 2004. Gypsum aquifer 3 Arsenic averages from USGS. 4 Eby 2004 Rhyolite aquifer 5 EPA 1997b 6 EPA 2001 7 Hoffer 1979 subsurface waters in 6 counties of western Texas. NA: not available.
26
Figure 2. El Paso City map showing the Franklin Mountains. Black lines divide different geologic formations and the blue line shows the Rio Grande River flow path. Colored circles show average arsenic concentrations in the El Paso city wells from year 1984 to 2003 (from EPWU 2003 archives). Different arsenic concentrations are denoted as follows: Red dots (largest): 16 to 32 ppb, Blue: 10 to 16 ppb, Purple: 8 to 10 ppb, Aqua: 5 to 8
ppb, smallest dots: less than 5 ppb.
Descriptive statistics of 26 variables (mean, median, standard deviation and range) for the entire
Hueco and Mesilla basins and sub-regions of them are presented in Table 2. Average
concentrations of As, Mn, Na, Na%, CO3-2, SO-2
4, SO4%, PO4-3 and pH are higher in the Mesilla.
Probably some proportions of Na and SO4 derive from the NM-gypsum aquifer discharged
through the river (which influences more the Mesilla than the Hueco groundwater). The Na% is
the percentage of Na relative to the sum of Ca, Na and Mg in milliequivalents (mEq) and SO4-2%
27
is the percentage of SO4-2 relative to the sum of Cl, SO4
-2 and HCO3
- in milliequivalents. The
Hueco Basin volume is about 4 times the Mesilla volume (considered here) and includes more
than 3 times number of wells (229) than the wells studied from the Mesilla (58).
Figure 2b. Spatial distributions of pH overlapped on As. pH is shown in triangles overlapping arsenic concentrations as circles (arsenic color code Figure 2a). The pH color code is: largest darkest red for ph between 9 and 10.1 (only one in Mesilla), orange circles from 8.07 to 9, green from 7.8 to 8.07, red from 6.8 to 7.8 and smallest pink less than 6.8 The median value of ph is 8.07. Arsenic colors concentrations in ppb: Red dots (largest): 16 to 32 ppb, Blue: 10 to 16 ppb, Purple: 8 to 10 ppb, Aqua: 5 to 8 ppb, smallest dots:
less than 5 ppb
28
Table 2. Descriptive statistics in the Mesilla and Hueco Basins from EPWU archives. (Units are in ppm unless otherwise indicated). Asterisks show statistically significantly larger average concentrations (0.05 Significance level)
Mesilla As* pH* Depth Fe Mn SiO2 Ca Ca%
Mean 12.4 8.4 323 247 148 31 42 19 Std. Deviation 5.2 0.45 235 277 202 6 33 8
Minimum 3.5 7.6 42 18 2 18 3 4
Maximum 27.8 10.07 950 940 769 43 139 37
Hueco As pH Depth Fe Mn SiO2 Ca Ca%
Mean 7.56 8.02 552 388 86 31 47 24 Std. Deviation 3.64 0.22 125 991 290 3 27 9
Minimum 1.15 7.05 147 6 1 16 17 11
Maximum 19.47 8.8 848 8756 2700 42 180 68
Mesilla Na Na% Mg Mg% Cl Cl% HCO3 HCO3 % Mean 167 77 7 5 123 35 132 23
Std. Deviation 74 11 7 4 80 8 73 7 Minimum 84 53 0.2 0 33 17 34 7
Maximum 455 95 29 16 467 57 347 48
Hueco Na Na% Mg* Mg% Cl * Cl% HCO3 HCO3 %
Mean 152 64 15 13 207 52 147 29 Std. Deviation 78 16 21 14 148 18 38 14
Minimum 4 2 3 3 25 14 0 0
Maximum 486 83 212 87 923 91 271 66
Mesilla SO4 * SO4 % PO4 TDS hardness Ec F NO3
Mean 195 42 0.21 652 130 1013 0.68 2 Std. Deviation 104 4 0.29 322 107 471 0.25 2
Minimum 51 29 0.02 249 10 407 0.14 0
Maximum 495 48 1.2 1530 461 2320 1.48 8
Hueco SO4 SO4 % PO4 TDS hardness Ec F NO3 *
Mean 90 19 0 640 167 1072 0.76 6 Std. Deviation 44 7 1 269 88 497 0.26 5
Minimum 31 7 0 241 63 1 0.23 0
Maximum 288 38 6 2004 584 3677 2.73 43
Mesilla K CO3 * N_wells Mean 4 6 58
Std. Deviation 2 11 Minimum 1 0 Maximum 10 55
Hueco K* CO3 N_wells Mean 9 0.89 229
Std. Deviation 3 1.16 Minimum 4 0 Maximum 22 10
29
Table 3. Spearman’s Correlation coefficients of arsenic (aqueous) with: pH, common ions, Fe, Mn, TDS, Hardness and depth, for the Hueco and Mesilla Basins. Observations aggregated by well from
EPWU archives Basin pH Fe Mn SiO2 Ca Ca% Na Na%
Mesilla .395** -0.281 -.627** -0.086 -.482** -.399** -.453** .488**
Hueco .410** -0.027 -0.076 -0.057 -.044 -.447** .486** .609** Hueco High potentiometric
.253 .156 .205 .425* .095 -.306 .441* .534**
Hueco Low potententiometric
.711** .170 -.335 .13 -.596** -.672** .216 .738**
Basin Mg Mg% Cl Cl% HCO3 HCO3 % SO4 SO4 %
Mesilla -.548** -.544** -.435** .156 -.450** -.053 -.478** -.203 Hueco -.300** -.637** .360** .334** -.373** -.421** .277** -.029
Hueco High potentiometric -.438* -.625** .487** .464** -.674** -.634** .277 .132
Hueco Low potententiometric -.71** -.727** -.022 .162 -.46** -.146 -.316 -.162
Basin Depth NO3 PO4 TDS K EC F CO3
Mesilla .532** 0.071 -0.253 -.470** -.357* -.411** 0.082 0.301
Hueco -.337** -.425** 0.125 .346** -0.046 .338** -.170* .191* Hueco High potentiometric -.083 .073 .185 .367* .424* .369* -.679** -.001
Hueco Low potententiometric -.115 -.104 -.227 -.137 -.561** -.079 .321 .56**
T-tests for independent samples between the two basins showed no statistically significant
differences in the means of: Fe, Mn, SiO 2, Ca, Na, HCO3-, F, PO4
-3, TDS, EC, and showed
significant differences for: As, pH, Cl, Mg, K, SO4-2
, CO3-2, NO3
- (0.05 significance level).
The Hueco Basin has a lower pH and lower arsenic concentrations than the Mesilla (Figure 2b).
A lower pH would be expected to result in lower arsenic concentrations if desorption from
hydroxide solids is a major control on arsenic concentrations. The significant difference in Cl
between the two acquifers probably comes from brine dissolution occurring in the Hueco more
than in the Mesilla. It is interesting to notice that HCO3- is similar even though pH and CO3
-2 are
not.
30
ASARCO is located in west central El Paso on the border with Mexico, and operated for over
100 years. Air borne As contamination is currently being remediated at over 400 home sites in
the area. The issue of groundwater contamination at ASARCO has never been adequately
addressed. No elevated groundwater As values reported in this study can be attributed to
ASARCO.
The Mesilla Basin.
The water studied in the Mesilla is a Na, SO4-2
water, since the proportions of those ions (relative
to the sum of major cations and anions respectively) are larger than the proportions of other
major constituents and also are larger than in the neighboring aquifers and in the river (Tables 1
and 2)
The most significant correlations for aqueous arsenic in the Mesilla Basin indicate that the deep
wells, that are not hydraulically connected to the river, have higher arsenic (Figure 3, Table 3).
These correlations are: with depth (positive and strong), with Na% (significantly positive), and
with pH (weak but statistically significant); the most significant negative were with: Cl, TDS,
Mn, Ca, Mg, Na, K, HCO3-, SO4
-2 and electrical conductivity (EC) (Table 3). The river and the
shallow wells influenced by the river have higher concentrations of TDS, Ca and SO4-2
and lower
pH and arsenic than the average Mesilla and Hueco groundwater. The low arsenic concentrations
are common for aerobic river waters where arsenic is present in the oxidized form arsenic (V),
which can be readily removed from the aqueous phase by sorption to a variety of minerals
31
present in sediments, such as iron and aluminum oxides. Figure 4 shows trends with depth in
both basins.
Arsenic is present as both arsenic(III) and arsenic(V) (Table 4a) but with at least half and usually
closer to two-thirds of the arsenic present as arsenic(V). The presence of As(III) indicates that
conditions are at least moderately reducing in the high arsenic wells, and reductive dissolution
may be happening. The predominance of As(V) indicates that either reductive dissolution is not
the sole mechanism mobilizing arsenic, or that substantial re-oxidation occurs subsequent to
mobilization. The lack of a correlation between aqueous iron and arsenic (Table 3) is puzzling.
It may be that reduction is sufficient to convert arsenic(V) to the less strongly adsorbed
arsenic(III) but not to mobilize substantial amounts of iron.
The correlation between arsenic(III) and pH is weaker than the correlation between arsenic(V)
and pH (Figure 5). The pH dependence of adsorption/desorption reactions is different for the two
species of arsenic. Arsenic(III) is neutrally charged at near neutral pH values and its sorption
reactions are not highly pH dependent. In contrast, the negatively charged As(V) desorbs from
metal hydroxide surfaces as the surfaces become negatively charged at high pH (Stollenwerk
2003, Sracek 2004, Smedley and Kinniburg 2002). Arsenic (total and two species) is correlated
with temperature, but not significantly, which is suggestive of a possible influence of geothermal
waters.
32
River Mesilla Shallow Mesilla all
500
400
300
200
100
0
Cl ppm
SO4 ppm
HCO3 ppm
Na ppm
Ca ppm
Figure 3. Average values of ionic concentrations in the River (horizontal axis), Mesilla Shallow wells (mixed with river waters), and the entire Mesilla Basin. Vertical axis shows concentrations in ppm.
33
W DeepW IntermediateW Shallow
Aquifer
p-value = 0.019
R = 0.35
Mesilla
0 10 20 30As archives
ppb
200
400
600
800
dep
thft
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Aquifer
0 5 10 15 20As archives
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hft
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WW
W
W
R = - 0.29p-value = 0.000
Figure 4. Scatter plot of arsenic (aqueous) with depth in both basins showing opposite trends in the
two basins. The correlation between arsenic and depth are opposite and weak. Depth scale is reversed.
34
pH
9.08.58.0
As
ppb
30
20
10
0
As 5 ppb
AS 3 ppb
As tot ppb
305
301
308
303
309
307
306
305301
308303
309307
306
305
301
308
303
309
307
306
Figure 5. Association be tween arsenic species and pH for 7 wells in the Mesilla Basin.
Table 4a. Arsenic speciation of 40 analyses performed by EPWU (2003) in 7 Mesilla intermediate confined wells.
Astot ug/L
As dissolved
ug/L
As(III) ug/L
As(III) %
As(V) ug/L
As(V) %
pH temp (C)
Mean 20.7 20.0 6.4 31.9 14.3 68.1 8.1 21.8 Median 18.5 17.7 6.3 28.9 12.4 71.1 8.1 21.6 Minimum 12 12.8 0.8 3.9 5.3 36.8 7.3 18.6 Maximum 37.1 36.3 12.5 63.2 31 96.1 9.2 25.7
Table 4b. Spearman’s Correlation coefficients for Speciation Analyses aggregated by well in 7 intermediate Mesilla wells (EPWU 2003).
As tot ug/L
pH AsIII ug/L
AsV ug/L
Cl
pH .519 AsIII ug/L .680 .144 AsV ug/L .859* .653 .247
Temperature C
.279 .823* .208 .220
Cl -.302 -.805* .072 -.455 F .715 .873* .484 .654 -.806*
35
The Hueco Basin.
The water in the Hueco has predominant concentrations of Na and Cl, and has higher
concentrations of Ca, HCO3-, Mg, K, F, NO3
- and hardness than the Mesilla Basin water studied
here (Tables 1 and 2).
In the entire Hueco Basin the most significant positive correlations of arsenic are with: pH, CO3,
SO4-2, Cl, TDS, Na, and electrical conductivity (EC) (Table 3). Negative and significant
correlations of arsenic are with: Ca%, Mg, HCO3-, depth, NO3
- and F. The positive significant
correlations with pH, Cl, SO4-2
and CO3-2 support competitive desorption as controlling arsenic
concentrations. High pH favors substitution of Cl, SO4-2
and/or CO3-2 for arsenic, desorbing it
from the aquifer solid surfaces into the groundwater. The significant negative correlations with
Ca, Mg and positive and significant with Na suggest that more heavily cation-exchanged waters
have higher arsenic levels, possibly because they are older and have had more opportunities to
encounter arsenic-bearing minerals (Anderholm and Heywood 2003, Sracek et al. 2004).
The negative association of As with HCO3- may be due to confounding with pH, given that low
pH favors bicarbonate over carbonate but would also lead to low arsenic by favoring sorption of
arsenic to iron (manganese or aluminum) oxides. Another possibility is calcite precipitation
along the flow path. Farther along the groundwater flow paths the pH and As may rise while
HCO3- decreases due to precipitation, as has been reported in other works (Anderholm and
Heywood 2002, Robertson 1989). The significant negative association of As with F may be
explained by geographic confounding. The arsenic is higher in the low potentiometric wells
36
(near the river) while fluoride is high in high potentiometric wells (near the mountains) (see
Table 5 and explanation of sub-regions below).
Spatially small regions imply short flow paths and generally less time for mixing and chemical
reactions. Longer flow paths allow more time for mixing and for chemical reaction. The Hueco
Basin was studied by observing its entirety and also 2 sub-regions: 1) close to the Franklin
Mountains, where water has higher proportion of more recent infiltration water with high
potentiometric levels and is expected to have traveled shorter flow paths) and 2) the region near
the river, where the water has low potentiometric levels and is expected to have traveled over
longer flow paths (Tables 5, Figures 6, 7).
The SO4-2 concentration close to the Franklin Mountains is three times smaller than under the
river (after the water has flown through the basin, probably dissolving gypsum). However the
correlation between sulfate and arsenic is stronger (and significant) closer to the Franklin
Mountains than farther, suggesting then that the mobilization of As in association with SO4
occurs more importantly close to the Franklin Mountains. A possible explanation is pyrite
weathering from the Franklin Mountain rocks releasing arsenic and sulfate into groundwater.
Another possible explanation is competition of SO4-2
for As sorption sites, supported by the
positive correlation between themselves and between each one with pH, along the basin. A
revision of mineral content in well cuttings and comparison with the Franklin Mountain
materials may consolidate the speculation that arsenic and sulfate come from the Franklin
Moutain rocks. As part of this work, the next chapter includes cuttings analyses for As, Fe and
TOC.
37
Throughout the length of the groundwater flow path (from the recharge toward discharge) the
strength of association of arsenic and pH varies. The positive correlations between arsenic and
pH have R_square = 0.14 close to the Franklins, R_square = 0.18 in the entire Hueco, but
R_square = 0.66 (p-values < 0.01) for the low-potentiometric head wells. These changes in the
strength of the association between the arsenic and pH may reflect the fact that over the longer
flow paths the levels of competing ions increase (carbonate, pH, and TDS are all higher in the
low-potentiometric head wells), increasing the importance of competitive desorption. In
addition, arsenic may become less random as there is more mixing and averaging out, so the pH
control is seen more clearly.
Other mechanisms may also play a role in arsenic mobilization. Eastoe (2004) found evidence of
evaporated waters in the Hueco, indicating that evaporation before infiltration and during
previous geological history might have contributed to creating water chemistry favorable to
arsenic mobilization.
The arsenic concentrations have no significant association with time either in the Hueco or the
Mesilla Basins. (The association between arsenic and time has R_square = 0 in the Hueco and
0.06 in the Mesilla). Analyses of time trends in individual wells yielded similar results.
38
7.6 7.8 8.0 8.2 8.4pH
0
5
10
15
20
As
ppb
W
WW
WW
W
W
W
W
W
W
W
W
W
W
WW
W 25
2628
3133
408
413
414
415
416
417
418
82
83
84
85
88
52
R-Square = 0.68p-value = 0.000
Figure 6. Relation between arsenic and pH in 18 wells in the Hueco. Numbers are EPWU Well#. Wells 52, 28, 25, 33, 31, 26 are high in the “North” parat of the city, East of the Franklin M. with high potentiometric level wells. Wells 408, 416, 414, 417 are “South” with low potentiometric levels and under the river, classified as artesian wells in EPWU records. The remaining wells are intermediate in location and potentiometric level.
South
North
39
North South
300
200
100
0
HCO3 ppm
Cl ppm
SO4 ppm
NA_PPM
Ca ppm
Figure 7. Variation in major ions dissolved in the Hueco, comparing wells in the North region (high potentiometric levels) (left) with the South region (low potentiometric levels) (right).
Table 5. Descriptive Statistics for different potentiometric level regions in the Hueco
Mean values
High Potentiomentric Levels, Shorter
flow paths 40 wells Table 5A
Entire Hueco
229 wells Table 2
Low Potentiomentric Levels, Complex larger
paths 53 wells
As ppb 5.4 7.6 10.5
Fe ppb 507 (824 outlier considered) 388 269
Ca % 29 24 23
Na % 58 64 67
Mg % 12 13 9
Cl % 48 52 57
HCO3 149 147 153
HCO3 % 33 29 21
CO3 .53 .89 1.19 SO4 81 90 129
SO4 % 19 19 22
F .86 .76 .69 TDS 596 640 812 pH 7.9 8 8.1
40
6.2 Statistical analyses of the additional water samples
The complete sets of analyses performed on the 99 water samples are given in the appendices. In
the Hueco Basin, the average (aqueous) arsenic concentration in the 91 wells was of 9.1 ppb
(median 6.8, ranging from 0.9 to 86.6 ppb and standard deviation of 11.5 ppb). The t-test for
difference of means did not show a significant difference between samples from this study and
archival values in the Hueco Basin. For the Mesilla Basin, the sample set showed slightly higher
arsenic concentrations from this sampling than the EPWU archives. A t-test for paired samples
shows that the maximum credible percentage of difference between the two data sets is about
17%. The archival information has a mean value smaller than the water sampled in this study.
As described above, field measurements of pH, dissolved oxygen and temperature were
measured for a set of 9 wells in the Juarez part of the Hueco, Table 6 summarizes these data.
Arsenic was strongly positively correlated with pH (Figure 8) and temperature in these wells (R
square = 0.98, P-value < 0.001, and 0.94, P-value < 0.007, respectively) and a negative
correlation with dissolved oxygen (R square = 0.32, P-value < 0.01). The correlation with pH
supports competitive desorption, while the negative correlation with DO may indicate a possible
role for reductive processes in mobilizing arsenic. The two highest arsenic observations were
measured in the lowest DO wells, and the odor of sulfide in one of these wells is an additional
suggestion of the role of reducing conditions. The strong correlation with temperature indicates
an influence from geothermal waters. The two confined wells sampled, denoted Artes1 and
Artes2 (Figures 8a and 8b), have arsenic concentrations of 87 and 70 ppb, the highest
observations in either of the two basins.
41
Table 6. Water sampling results for 9 samples from the Juarez lower valley part of the Hueco .
AqueousAs ppb
PH Temperature C
EC µS
DO ppm
Mean 16.6 7.98 24.9 1406 3.17 Median 9.3 7.80 23.3 1153 3.03 Minimum 0.90 7.66 18.6 667 0.10 Maximum 86.6 8.80 35.7 2313 6.70
7.8 8.0 8.3 8.5 8.8pH
0
25
50
75
as_t
rip
s
W
WWW
W
W
W
W
W
town17
town62
town9loweV3
loweV4
artes1
loweV2
artes2
sierra
R-Square = 0.98p-value = 0.000
Figure 8a. Scatter plot of aqueous arsenic versus pH for 9 wells sampled at Juarez city. Results are
given in Tables 6 and 7.
42
20 25 30 35Temperature
C
0
25
50
75A
s (a
q)
ppb
W
WWW
W
WW
W
W
town17
town62
town9sierra
artes1
loweV2loweV3
loweV4
artes2
R-Square = 0.89p-value < 0.01
Figure 8b. Scatter plot of aqueous arsenic versus temperature for 9 wells sampled at Juarez city.
Results are given in Tables 6 and 7. 7. Discussion of Archival Analyses
In the El Paso region, the likely source of arsenic in the aquifers is the arsenic in the sediments
deposited in part from the Franklin Mountains where some rocks belonging to the Castner, Red
Bluff and Lanoria formations have arsenic concentrations between 18 and 280 ppm
(McCutcheon 1982), much higher than the Earht’s crust average of 2ppm (Siegel 2002).
43
The distribution of arsenic in the Mesilla Basin is largely controlled by depth, with low arsenic
water from the Rio Grande River overlying deeper and older water with higher pH and arsenic
concentrations. Reductive dissolution is suggested to occur in some proportions in the Mesilla
wells since the content of arsenic(III) is 32% and competitive desorption is suggested by the
positive correlation with pH. In summary, evaporative concentration, competitive desorption,
influence from geothermal systems and reductive dissolution may also influence the arsenic
concentrations but less dramatically than the distinction between the young river water and the
older, deeper water. Information on the age of the water could confirm this conclusion.
In the Hueco Basin, the significant positive correlation coefficients between dissolved arsenic
and: pH, sulfate, and carbonate support competitive desorption, but evaporation may promote
high arsenic also since pH, chloride, sodium are positively correlated with arsenic and would all
be concentrated in an evaporated water. Desorption of arsenic from aquifer solids may occur
gradually along the path of flow similar to the exchange of divalent cations are exchanged for
monovalent ones (Ca and Mg for Na and K). This would expla in the positive and significant
correlation of As with Na and K, and at the same time the negative significant of As with Ca and
Mg. A geothermal system is highly likely to exist underneath the river (in the Juarez portion of
the Hueco that was sampled) and to be the source of the high arsenic (up to 87 ppb) there.
However, further sampling is needed to delineate the extent of geothermal influence. The
presence of elevated As values in the southeast portion of the area of study has not been
explained and more experiments appear reasonable to help inducing a clear model of it.
44
Arsenic desorption from iron hydroxides does not require a positive correlation between arsenic
and iron dissolved in aqueous phase. Previous researchers have speculated (Robertson 1989)
iron control on arsenic by a correlation between iron in solid phase and arsenic aqueous. In the
current work, it was found that iron in solution is not consistently correlated with arsenic. Since
no solid phase data were found in the archives, the analysis of solid phase aquifer materials for
arsenic, iron and total organic carbon (TOC) was identified as a research priority to test the
competitive desorption as mechanisms controlling arsenic in the groundwater in the El Paso
region.
Isotope analyses could inform the age of the waters and possibly the sources of them, and
together with common water chemistry, dissolved oxygen, temperatures and depths may quantify
the proportions of mixing, upwelling or evaporation in the waters. Future work collecting
information about water chemistry, depths, speciation, etc, is warranted to identify definitively
the mechanisms of arsenic mobilization in different sub-regions, as well as to characterize the
geothermal system in the Hueco (under Juarez) including its arsenic concentrations and spatial
extent.
45
Chapter 4.
Augmenting the Archival Data: Cuttings and Leaching Experiments.
1. Arsenic Desorption in Groundwater.
Desorption of arsenic from the aquifer solid surfaces is considered by some authors as the
predominant control on arsenic concentrations in many groundwater systems (Stollenwerk
2003). Particularly in the Hueco Basin, where statistical analyses inform that arsenic aqueous is
significantly correlated with pH, chloride, sulfate and carbonate, desorption is a strongly
suggested candidate for controlling arsenic in most of the water, other processes may also
contribute to arsenic mobilization and processes may work to varying degrees in different sub-
regions or at different times.
The adsorption processes are mainly produced by electrostatic forces between the surfaces of the
rocks or sediments, and the ions dissolved in the water. (e.g. Cl, I, Br, Na, NO3-1, CO3
-2, ClO4-1,
As, F, Cu, Pb, PO4-3, AsO4
-3) (Foster 2003, Stollenwerk 2003). Some anions are known to
compete with the arsenic molecules for sorption sites on the aquifer solids. If an anion displaces
an arsenic molecule, the arsenic will desorb from the solid phase passing into the aqueous phase,
increasing the dissolved arsenic concentration. The molecules that usually occur in groundwater
and compete with arsenic oxide (with different strengths) are: SO4–2, CO3
-2, SiO2, PO4 –3, OH-
46
and F. These species may occur in concentrations orders of magnitude higher than typical arsenic
concentrations, thereby producing a considerable competitive effect (Holm 2002, Sracek et al.
2004).
Equilibrium chemistry models show a sharp increase in the amount of arsenic(V) desorbed from
iron hydroxides for pH >= 8 (Montoya and Gurian 2004). Experimental evidence (Foster 2003,
Stollenwerk 2003) confirmed that arsenic complexation mechanisms are relatively independent
of the solution ionic strength but strongly dependent of the solution pH with the arsenic(V)
desorption increasing with higher pH.
In this section, cuttings from wells where arsenic in water was high and low (in both basins)
were analyzed, also those sediments were leached with two different pHs to find the proclivity of
arsenic desorption from them at high pH. The goals were 1) to find whether there exist different
sediment compositions between wells with high and low dissolved arsenic, or different leaching
proclivity, 2) to evaluate the local origin of arsenic in solution, and 3) to gain insight into which
processes are responsible for controlling arsenic concentrations.
2. Cuttings Experiment
As the archival data did not contain information on the solid phase aquifer materials, a set of well
cuttings was analyzed for arsenic, iron and total organic carbon (TOC). Correlations between
solution and solid phase arsenic would suggest a local source (or sink) for arsenic mobilized into
47
the water, while the lack of such correlations would indicate that mobilization mechanisms
operate over a larger spatial scale. In other words, if the arsenic content of the cuttings is not
associated with the arsenic content in the water, then the high arsenic concentrations in water are
mobilized from minerals located far from the wells and carried into the water. In addition,
positive correlations between the iron and the arsenic concentrations in the solid phase would
support iron control on the arsenic concentrations in water (by desorption from iron hydroxides
or dissolution from sulfides or oxides).
3. Leaching from Cuttings Experiment
If arsenic desorption is the predominant mechanism controlling arsenic concentrations in the El
Paso region, it is expected that leaching aquifer materials at high pHs would desorb arsenic from
the solid phase, augmenting the statistical information with experimental results. To evaluate the
proportion of arsenic desorbed from the solid material of the aquifer, an experiment was
performed consisting of measuring the amounts of arsenic leached from the cuttings into
different pH solutions. The concentrations of arsenic in the solid material (well cuttings) were
measured as described in the previous section. The arsenic leached is hypothesized to be
desorbed from the iron hydroxides. The experiment was performed with two pHs: one solution at
pH 9 and another at pH 10. It is expected that the proportions of arsenic leached into solution
will follow the trend for desorption as a function of pH found in the Montoya and Gurian (2004)
computer model.
48
4. Methodology
The map in Figure 1 shows the wells in yellow circles and their well numbers whose cuttings
were leached and analyzed (well numbers are in black). The 5 wells in the Mesilla Basin (in the
Canutillo field) are in the cluster west of the Franklin Mts. The Hueco wells are located east and
south of the Franklin Mts. Table 1 shows the individual wells and depths analyzed and the
laboratory results. Chapter 3 provides descriptive statistics of wells in both basins. Eleven wells
where selected but for some of them more samples were chosen corresponding to different
depths.
4.1 Well Cuttings selection and preparation.
Cuttings from the wells drilled by the El Paso Water Utilities (EPWU) have been archived in the
Geological Sciences Department of the University of Texas at El Paso. Samples of
approximately 1.5g of material from each of the 15 cuttings were analyzed for arsenic and iron.
Each sample was thoroughly sieved to 75 microns size (standard sieve # 200) before analysis in
the NMSU SWAT laboratory. Total organic carbon (TOC) was analyzed in 11 cuttings. The
sample size is 15 (from 11 wells) including both basins, the Hueco and the Mesilla (Table 1).
49
Figure 1. El Paso city map with well numbers of the cuttings leached and analyzed for As, Fe and organic carbon in solid phase. Arsenic concentrations in wells are shown by colored dots with size proportional to arsenic (aq) content. The Franklin Mts rocks with As are shown in color. Wells 67 and 10 have very small dots sizes and Wells 108 and 111 are behind the 305. Explanation for color of dots: Red: aqueous arsenic 22 to 32 ppb, Bright Blue: 14.7 to 22 ppb, Purple: 10.5 to 14.7, Aqua blue: 8.2 to 10.5, pink and black: less than 8.2 Rock color explanation: red up to 64 ppm, blue up to 280 ppm, yellow 18 ppm. The locations and arsenic concentrations of the 11 wells are shown in Figure 1. Of the entire 15
samples, 10 were from high (11 ppb to 27 ppb) groundwater arsenic and 5 from wells where
groundwater arsenic was lower than 6 ppb. From the Mesilla, 5 wells were selected, 2 with low
arsenic in groundwater (108, 111 in the shallow aquifer), and 3 with high arsenic (112 in the
shallow aquifer, 305 and 306 at two and three depths, respectively, from the intermediate
aquifer), to complete 8 cuttings. In the Hueco, 6 wells were chosen, 3 with low arsenic (10, 28
Mesilla Basin
Hueco Basin
50
and 67) and 3 with high arsenic concentrations (9, 68 and 408), to complete a set of 7 cuttings
(well 9 was analyzed at two different depths).
Wells# 108, 111, 112, 10, 28, 67 and 68 were analyzed at only one depth, the closest one to the
screen depth range available in the archives. The wells 305, 306 and 9 were sampled in two
different depths. There was special interest in Wells 305 and 306 because speciation data is
available for these wells, and there was special interest in Well 9 because these cuttings have the
darkest color, indicating high iron or organic matter content. Appendix B describes in detail
each one of these three wells and has the complete speciation analyses for Wells 305 and 306
(EPWU 2003 archives). See Table 1 for the well numbers and depth and Figure 1 for sampling
locations.
Figure 2. Well #9 with highest Fe content shows dark color clays (sample from 290’ deep on left, sample from 540’ in center). Well #112 (right), as the rest of the samples, has clear sand color.
51
4.2 Leaching experiment.
The same wells and depths analyzed for cuttings were also analyzed for arsenic leached into
solution. From the same EPWU well cores archived in the UTEP Geological Sciences Dept. a
total of 30 samples were leached for two days and analyzed for arsenic. The 30 samples came
from 15 different sediments each leached at two pHs. Table 1 shows the set of individual wells
with their depths, the pH of leaching and the arsenic leached in parts per billion (ppb) as well as
in percentages (of the arsenic content in the solid phase referred to above).
For each one of the 15 cuttings, approximately ten grams were taken, one half was sieved to 75
microns size (standard sieve # 200) and one half was left as it was in the archives. The two
halves (the sieved and the not sieved one) were reunited and blended to compile a more or less
homogeneous mix. Pingitore et.al. (2002) studied samples of pebbles from the region leached in
laboratory and analyzed for Pb and As and showed that As and Pb leached more efficiently from
samples sieved through 75 micron screens than from the raw material texture.
For each well, the mix (sieved and not sieved) was split into two samples, one to be leached at
pH 9 and one at pH 10. Each solution was made of 15 ml of DI water adjusted with NaOH to the
pH.
The total set of 30 sand samples was leached for two days while being shaken mechanically, then
centrifuged (3500 – 45000 rpm, for 15 – 25 min), filtered and analyzed for arsenic. The aqueous
phase separated by the centrifugation varied between 7.9 and 13 ml, showing that the sediments
adsorbed the solution differently (13% to 47%). Sediments from well 9 were dark indicating
52
either high organic matter or Fe (Figure 2). All the other sediments were a light brown color
typical of sand.
5. Results of the Cuttings Experiments
Notice that wells 305, 306 and 9 were sampled at different depths: shallow (s), intermediate (i) or
deep (d). Tables 1 and 2 show the composition of the cuttings individually and summarized by
sub-regions inside each basin. Figures 3, 4 and 5 show scatter plots for As aqueous, As in solid,
Fe in solid, TOC in solid. Logarithm transformations were taken for non-parametric correlation
coefficient calculations.
The arsenic concentrations in well cuttings with high aqueous arsenic are statistically higher than
in cuttings with low aqueous arsenic, also the correlation between aqueous and solid arsenic is
positive (Spearman’s correlation coefficient 0.4). All this suggests that concentrations of arsenic
in solution are controlled locally by the sediments of the aquifers (Tables 1, 4 and Figure 3)
(although it is possible that an unknown part of the arsenic derived originally from distant
sources).
The concentrations of As and Fe in the cuttings are positively correlated, and also As and Fe
(solid) concentrations are statistically higher in high arsenic water samples than the
concentrations in cuttings from low arsenic in water (Table 4, Figures 3, 4 and 5). Organic matter
was similar in both sets. At the same time, the correlation between As in water and Fe in the
53
solid phase is significant and strong (Spearman’s correlation = 0.7, p-value < 0.01, Table 3).
Therefore, it may be hypothesized that iron in solid phase exerts control over arsenic
concentrations in the water and in the solid phase.
Arsenic leached (in ppb not in percentage) was associated with As in both water and cuttings
strongly and significantly, and with Fe, not significantly (Table 3 and Figures 3 to 6). The
arsenic concentrations leached from pH 9 as well as solid phase As and Fe, are statistically
higher in samples with high arsenic aqueous (Table 4). A non-significant correlation was found
between Fe solid phase and As solid phase (Spearman’s Ro = 0.4, p-value > 0.05) (Table 3).
All this is consistent with adsorption to iron controlling arsenic in solution.
Iron concentrations in solid phase varied considerably (the standard deviation of 5840 ppm is
higher than the median of 4840 ppm), the highest values (19400 and 19700 ppm) are in the
Hueco well 9, where organic carbon (TOC) increased sharply with depth (from 0.26 and 1.4)
(Tables 1, 2, Figure 5). Well #9 has remarkable dark sand color that indicates its high iron values
(Figure 2).
A t-test for independent samples showed no statistical difference between the Hueco and the
Mesilla compositions of any variable (Table 4b). From this, the two basins appear to be filled
with similar sediments (regarding As, Fe and TOC), which was expected since they are
neighboring basins surrounded almost by the same mountains and crossed both by the river.
54
Figure 3 shows a scatter plot for the logarithm of arsenic in solid phase and pH 10. A comparison
of the amount of arsenic leached at pH 10 and pH 9 indicated that more arsenic was leached at
pH 10, but a t-test for independent samples found that this difference was not statistically
significant (t-statistic = 0.68, significance = 0.50). Total organic carbon (TOC) was not
correlated with the amounts of arsenic leached. None of the correlations between aqueous
arsenic, percentage of arsenic leached, nor depth were significant.
None of the 15 solid phase samples have low As, Fe and TOC in the solid phase and high
aqueous As. Among the wells with high aqueous arsenic, the Wells 68, 112 and 408 have low
arsenic in the solid phase (cuttings) but the 112 and 408 have Fe (and TOC) higher than the
median. Well 68 has close to the median Fe and TOC values.
In the Mesilla all wells have cuttings with similar arsenic, iron and organic carbon in solids. In
addition, the arsenic leached is almost equal for all. However the dissolved arsenic
concentrations are statistically different (Chapter 3). Shallow wells 108 and 111 yield
consistently low arsenic in water (5.7 ppb), while 112, 305 and 306 yield higher than 14 ppb
consistently. The shallow wells are influenced by the river which dilutes their water. Well #112
yields high aqueous arsenic despite being shallow, possibly due to its relatively high TOC.
55
Table 1a. Individual wells and depths with the cutting analyses and Leaching experiment results. Bold values are higher than the me dian.
WELL# Cuttings depth ft
As (aq) archive
ppb
Fe sand ppm
As sand ppm
Leached at pH 9 ppb
Leached at pH 10 ppb
TOC sand %
EPWU 10 510 5.5 4530 3.4 1.1 (11%) 0.71 (8%) 0.75 EPWU 108 140 3.5 4840 2.6 1.23 (11%) 0.44 (4%) NS EPWU 111 140 5.7 10400 4 3.1 (36%) 2.98 (39%) NS EPWU 112 140 13.5 5175 2.5 2 (19%) 0.84 ( 7%) 0.79 EPWU 28 530 4.35 1820 3.5 0.49 (6%) 0.84 (10%) 0.33 EPWU 305 120 NA 3480 4.7 2.23 (37%) 1.9 (32%) 0.48 EPWU 305 300 26.81 9930 5.2 2.0 (38%) 0.95 (18%) 0.66 EPWU 306 100 NA 4770 4.7 1.34 (21%) 2.19 (35%) NS EPWU 306 330 12.77 3020 5 NS 1.92 (33%) 0.48 EPWU 306 460 12.77 9760 10 1.07 (37%) NS < .01 EPWU 408 330 13.35 5110 2.4 2.0 (18%) NS NS EPWU 67 440 <0.4 2810 2.7 1.45 (12%) 0.69 (6%) 0.08 EPWU 68 440 10.88 4060 < 0.3 3.74 (4%) 9 (9%) 0.38 EPWU 9 290 NA 19400 5.5 1.01 (26%) 1.17 (31%) 0.26 EPWU 9 540 15.5 19700 7.4 0.79 (18%) 1.21 (31%) 1.40
NA: Not available NS: Not sampled
Table 1b. Statistical summary for the solid-phase analyses.
As (aq) archive
ppb As sand
ppm Fe sand
ppm TOC
sand % cuttings depth ft
Mean 10.7 4.3 7254 0.51 364
Median 12.2 4.0 4840 0.48 440
Std. Deviation
7.28 2.4 5643 0.39 168
Minimum < .4 < .3 1820 <.01 100
Maximum 26.8 10.1 19700 1.40 590
56
Table 2. Results aggregated by location of leaching (pH 10) and solid phase analyses for As, Fe and TOC. Method detection limit (MDL) showed for each variable with same units.
Samples Location
well# (feet different depths)
Solution As archive
ppb (MDL 0.4)
Solid Phase
As ppm
(MDL 0.3)
As leached
(aq) %*
Solid Phase
Fe ppm
(MDL 1)
TOC %
(MDL 0.01)
All 15 samples 9 (290', 540'), 10 (510), N 15 15 15 15 11
68(440),108,111,112,67(440) mean 11.9 4.26 2.47 7254 0.509 305(100',300'),28(530) min < 0.4 < 0.3 0.44 1820 < 0.01
306(100',300',460'), 408(330) max 26.8 10.1 18 19700 1.4
Mesilla 112, 305 (300') N 4 4 4 4 4
High As(aq) 306(300', 460') mean 16.16 5.7 1.19 6971 0.48 min 12.17 2.5 0.84 3020 < 0.01
max 26.81 10.1 1.92 9930 0.79
Mesilla 108, 111 N 2 2 2 2 0
Low As(aq) mean 4.6 3.3 1.71 7620 NS NO TOC sampled:108,111 min 3.5 2.6 0.44 4840
max 5.7 4 2.98 10400
Hueco 9 (290', 540') N 4 4 4 4 3
High As(aq) 408, 68 mean 13.8 3.9 5.61 12068 0.68 NO As_sand detected: 68 min 10.9 < 0.3 1.17 4060 0.26
NO TOC sampled:108,111 max 15.5 7.4 18 19700 1.4
Hueco 10, 28, 67 N 3 3 3 3 3
Low As(aq) mean 3.28 3.2 0.747 3053 0.39 min < 0.4 2.7 0.69 1820 0.08
max 5.5 3.5 0.84 4530 0.75
Both basins 112 305( 300) N 7 7 7 7 6 Close to the River 306 ( 330, 460), mean
15.57 5.44 1.32 10299 0.598
High As(aq) 9(290, 540), 408 (330) min 12.2 2.4 0.84 3020 < 0.01
max 26.8 10.1 2.05 19700 1.4
Both basins 10, 108 (140), 111(140), 67 N 4 4 4 4 2 Close to the River mean
3.68 3.18 1.21 5645 0.415
Low As(aq) min < 0.4 2.6 0.44 2810 0.08
max 5.7 4 2.98 10400 0.75
Both basins 112 N 1 1 1 1 1
Recharge mean 13.5 2.5 0.84 5175 0.785
Frk Mt & Rio min
High As(aq) max
Both basins 108, 111, 28 N 3 3 3 3 1
Recharge mean 4.52 3.37 1.42 5687 0.33
Frk Mt & Rio min 3.5 2.6 0.44 1820
Low As(aq) max 5.7 4 2.98 10400
leaching* at pH = 10 for two days NS: not samples analyzed
57
Table 3. Spearman’s Correlations of Cuttings and Leaching variables for both Basins. Log transform. For 14 wells, excluding well#68 (not detected arsenic in solid phase).
Spearman’s As aqueous As solid Fe solid TOC solid
As leach pH 9
As solid .410 Fe solid .714** .431
TOC solid .477 .182 .559 As leach pH 9 .612* .663** .520 .183 As leach pH10 .543 .645* .223 .098 .618*
* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
58
Table 4a. Comparison between High and Low arsenic (aq) for the cuttings and Leaching results. Statistics, Confidence intervals and Significance for the t-test of difference of means, equal
variances assumed. Bold the significant different means differences.
Variables
High As(aq)
N Mean Std.
Deviation
T-test Significance Difference of Means
95% Confidence Interval of the
difference lower upper
As archives 1 Low As 6 3.83 2.06
Ppb High 10 14.55 4.89 0 - 15.3 - 6.17
As solid 2 Low As 5 3.24 .59
Ppm High 7 5.44 2.7 .107 -4.97 0.567
Fe solid Low As 5 4880 3326
Ppm High 7 10299 6804 .134 -12823 1985
TOC solid Low As 3 .387 .338
% High 6 .597 .483 .526 -.96 0.54 As %leach 2 pH 9
Low As 5 1.46 .966
% High 6 1.49 .592 .948 -1.1 1.04
As %leach 2pH 10
Low As 5 1.13 1.04
% High 5 1.22 .422 .87 -1.25 1.08
As leach pH 9
Low As 6 17.7 12.2
Ppb High 9 24.3 8.44 .231 -18.12 4.79
As leach pH 10
Low As 6 17.17 15.90
Ppb High 7 22.14 9.91 .505 -20.87 10.92 1 Samples include only the wells for the cuttings and leaching experiments. For well# 305 at 120’ and well# 306 at 100’ are not
arsenic dissolved archival data. 2 For Well#68, arsenic solid was not detected, therefore the leaching percentages were not calculated.
59
1.5 2.0 2.5 3.0 3.5Ln As leached
pH10
1.0
1.5
2.0
Ln
As
solid
W
W
W
W
W
W
W
WW
W
W
W
10
108
111
112
28
305s
305i
306s
306i
67
9Ai
9AdR-Square = 0.71p-value < 0.05
Figure 3. Arsenic in solid phase correlated with the arsenic leached (in ppb) from all the clays at pH
10 (p-value = .01). Labels represent well#. Arsenic in solid phase was not detected in well #68.
60
MesillaHueco
0 3 5 8 10As solid ppm
0
6
12
18
24
As
aqu
eou
s
ppb
W
W
W
W W
W W W
108 111
112
305s305i
306s 306i306d
WW
W
W
W
W W
10
28
408
67
68
9Ai 9Ad
R-Square = 0.17
Figure 4. Scatter plot of arsenic aqueous as register in archives (ppb) versus arsenic in solid phase (ppm) after analysis in this work. Regressions calculated separately in each basin give only a slight association between aqueous and solid arsenic for the Hueco Basin. Well numbers are shown.
61
MesillaHueco
5000 10000 15000 20000Fe solid ppm
0.00
2.50
5.00
7.50
10.00
As
solid
ppm
W
W
W
W
W
WW
W
108
111
112
305s
305i306s
306i
306d
WW
WW
W
W
W
10 28
408
67
68
9Ai
9AdR-Square = 0.19
R-Square = 0.67p-value 0.013
Figure 5. Arsenic and iron in solid phase are significantly correlated for the Hueco but not in the
Mesilla nor in the entire sample population (Table 3). Well numbers are shown and subscripts refer to depths: “s” shallow, “i” intermediate and “d” deep. Notice that Fe values in Well # 9 are far from
the entire population, and Well # 306d has by far the highest As. Mesilla wells have higher As values that the Hueco in general (except well 9).
As analytical results show, solid-phase iron and pH are at least the median value for all the wells
that yield high arsenic in water while they are smaller than the median for the wells that yield
low arsenic (Figure 6).
62
Figure 6. Sites with solid iron and pH above the median (orange triangles) compared with sites
with solid iron or pH below the median (green triangles). Aqueous arsenic shown by circular dots Different arsenic concentrations are denoted as follows: Blue dots: 10 to 16 ppb, Aqua: 5 to 8 ppb,
smallest dots: less than 5 ppb (Table 1)
Fe solid and pH over As aqueous
63
6. Conclusions
Solid-phase iron controlling the arsenic geochemistry is strongly suggested from significant
associations between aqueous arsenic, iron in the solid phase, and arsenic leached at pH 9 (less
strongly with pH 10) and these associations (Table 3) coupled with the statistically significant
correlations in the Hueco of arsenic in water with pH, sulfate, chloride and carbonate (Chapter
3), suggest arsenic desorption from iron hydroxides as an important mobilization factor of high
arsenic in groundwater in the Hueco Basin.
The materials in both basins appear to belong to the same statistical population (Table 3) this is
expected if the basins fill is from natural, randomly dispersed material, as opposite to local
occurrences of high arsenic in the solids or water of the aquifer, in other hand, the water
concentrations in the maps (Figures 2 chapter 3 and Figure 1 chapter 4) show no high punctual
concentrations close to any anthropogenic source. The presence of elevated As values in the
southeast portion of the area of study has not been explained in detail, but the iron control of the
arsenic geochemistry is suggested in all the region.
The positive and significant correlations of arsenic leached with both: arsenic in water and in
solid phases, also coincide with associations found if solid phase arsenic is the source of the
arsenic in water. Then it is suggestive that the Franklin Mountains are important local source of
arsenic in the aquifers (The granitic formations in the Franklin Mountains hold up to 281 ppm of
arsenic (Mc Cutcheon 1982), which exceeds in 140 times the Earth’s Crust average of 2 ppm
(Smedley and Kinniburg 2002), however because the association aqueous-solid arsenic, is weak
64
(Table 3 chapter 4, correlation coefficient 0.43), the suggestion should be strengthened with
more information such as sampling of water and cuttings close to the potential anthropogenic
sources and comparison of their arsenic concentrations with the rest of the aquifers.
From the experiments performed here, statistical analyses show no difference in the sediment
compositions in the Hueco and Mesilla Basins regarding arsenic, iron and organic mater. From
the archival information processed in Chapter 3, the mean concentrations of arsenic dissolved
from well waters of the two basins, are statistically different. The Mesilla Basin yields higher
concentrations of arsenic in water, and although the difference is significant and consistent, it is
moderate (mean in the Hueco = 7.56 ppb, in the Mesilla = 12.4 ppb, Table 2, Chapter 3). The
sediments in the Mesilla Basin have more proclivity to leach arsenic at higher pHs than in the
Hueco, which may explain the statistical difference between arsenic concentrations in the water
between both basins. At pH 9 the difference is statistically significant (p-value = 0.015) while at
pH 10 the difference is not significant.
Interesting future work could include additional leaching experiments to obtain greater power
with larger sample sizes, assessing geothermal influences with additional measurements of
temperature, and testing the reductive environment by measuring dissolved oxygen, major redox
species (nitrates, phosphates, sulfates, sulfides), and additional arsenic speciation analyses in the
water and solid phase.
65
Chapter 5.
Final Conclusions
In this work two case studies assessed environmental problems by using databases originally
intended for different purposes. Two experiments were performed augmenting the archival
information to better understand the arsenic occurrence in the groundwater case.
These case studies were addressed by statistical analyses, one for air quality and one for arsenic
contamination of groundwater. The former is national in scope, addressing air quality using
statewide archives; the latter is regional in scope, addressing water quality using 6400 local
observations. For the first case study statistical techniques are used to clarify intriguing
correlations between the diabetes occurrence and the air pollution emissions from statewide
averages. It was found that diabetes occurrence per state is more directly correlated with the
percentage of Afro-American population than with the air pollution per state. More Afro-
Americans usua lly live in states of the United States that are highly industrialized probably
because historically these areas offered employment opportunities for African-American
migrants from rural southern areas. Aftrican-Americans have shown statistically greater
proclivity for diabetes occurrence (National Institute of Diabetes and Digestive and Kidney
Diseases NIDDK 2003). This difficulty in distinguishing between the effects of air pollution and
ethnicity because of correlation between these two factors is called confounding. This indirect
association causes the bivariate correlation between air pollution and diabetes to be significant.
66
This case study shows that conclusions based upon statewide averages may serve to guide in
heath, environmental, industrial or urbanization policies, more than to link causes to health
problems.
For the second case study, the goal was identify the origin of high arsenic (> 10 ppb) and factors
mobilizing it into the groundwater of the El Paso region. Two cost effective solid phase analysis
experiments were designed and performed augmenting the archival information to test the
hypotheses of local origin and high pH desorption developed from the archival data. Fifteen well
cuttings were analyzed for arsenic, iron and total organic carbon. In addition, they were leached
in pH 9 and 10 solutions. Solid iron control over dissolved arsenic in the aquifers is indicated by
the results of these experiments (correlation between solid Fe and aqueous As Are significant at
the 99% confidence leve l).
In order to support desorption of arsenic from iron hydroxides at high pH, one would expect
significant associations between arsenic leached from the cuttings at high pH (9 and 10) and
dissolved arsenic, solid arsenic and solid iron. Here all but the last were found to be correlated
positively but not always significantly (Table 3, Chapter 4). These results suggesting desorption
are also in agreement with the statistical analyses of the water from archives (positive significant
correlations in water between As and: pH, Cl, SO4-2, CO3
-2, TDS) (Table 3, Chapter 3). Results
from statistical analyses of the archival and experimental data both agree with the model of
arsenic desorbing from solid iron into the groundwater when pH is high (> 8.5).
In this dissertation, statistical techniques have been used to organize and interpret archival
information. The analysis of the archival data has in turn guided experiments and led to useful
67
conclusions. In the first case these conclusions may guide environmental policies suggesting
regional priorities for health studies and interventions. In the second case, the results supported
the hypothesis that desorption is a factor likely causing high arsenic in the parts of the region and
that solid iron is probably controlling the arsenic concentrations in water in the region.
68
References
Anderholm S K and Heywood C E, 2003 “Chemistry and age of ground water in the southwestern Hueco Bolson, New Mexico and Texas” U.S.G.S Report 02-4237. Baxfield L M, 2001 “Occurrence and sources of arsenic in ground water of the middle Rio Grande Basin, central New Mexico”, M.S. Thesis, Hydrology, New Mexico Institute of Mining and Technology, Socorro, New Mexico. Berg M, Tran H C, Nguyen T C, Pham H V, Schertenleib R and Giger W, 2001 “Arsenic contamination of groundwater and drinking water in Vietnam: a Human health threat”, Environment Science and Technology, Vol. 35(13). Eastoe C, 2004 “Isotopes in the Hueco” Presentation EPWU Hueco Basin/Rio Grande Aquifer Meeting, El Paso, TX. Eby G N, 2004 Principles of Environmental Geochemistry, Brooks Cole. EPA-600/4-91-010 “Methods for the Determination of Metals in Environmental Samples”, Environmental Monitoring Systems Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio 45268, June 1991. EPA 1997 Transboundary Aquifers of the El Paso Ciudad Juarez Las Cruces Region. FinalReport. EPA 1997b Second Phase of the Binational study regarding the presence of Toxic substances in the Rio Grande along the boundary portion between the United States and Mexico Vol II. EPA 2001, Federal Register part VIII, 40 CFR 2001, 66(14) EPWU 2003 “El Paso Water Utilities report on line” at http://www.epwu.org Fisher R S and Mullican W F, 1990 “Integration of ground water and vadose-zone geochemistry to investigate hydrochemical evolution: a case study in arid lands of the northern Chihuahuan Desert, Trans-Pecos Texas” Bureau of Economic Geology, University of Texas at Austin. Heinrichs G and Udluft P, 1999 “Natural Arsenic in Triassic rocks: a source of drinking water contamination in Bavaria, Germany” Hydrogeology Journal 7: 468. Hoffer Jerry M 1979 “Geothermal exploration of western Trans-Pecos Texas” Texas Western Press, Science Series UTEP.
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Holm T R, 2002 “Effects of CO3, HCO3, Si, and PO4 on Arsenic sorption to HFO” Journal AWWA, 94(4). Lockwood A, 2002 “Diabetes and the air pollution” Diabetes Care 25:1487-1488. Lockwood A, 2002 “Response to Nicolich” Diabetes Care 25:2367-2368. Benitez-Marquez E, B R Diaz, and P L Gurian, 2004 “Understanding the Associations Between Statewide Diabetes Prevalence and Air Pollution Emissions,” Diabetes Care, 27 (6):1515-1517. McCutcheon T J, 1982 “Petrology and geochemistry of the Precambrian red bluff granite complex at Northern Franklin Mtns. El Paso Texas” UTEP MS Geology Thesis. Montoya T and Gurian P L, 2003 “Numerical Solution of a Chemical Equilibrium Model of Arsenic Sorption to Ferric Hydroxide” Proceedings of the Modeling and Simulation Workshop of the International Test and Evaluation Association. Montoya T and Gurian P L, 2004 “Modeling Arsenic Removal by Coagulation with Ferric Salts: Effects of pH and Dosage” Proceedings of the Texas Water 2004 Conference, Arlington, TX. Nicolich M, 2002 “Diabetes and the state capital” (Letter). Diabetes Care 25:2367 Pingitore N, 2002 “Environmental Geology, class presentation” UTEP, ESE Program. Robertson F N, 1989 “Arsenic in ground water under oxidizing conditions, south west United States” Environmental Geochemistry and Health 11:171 Selinus O, Alloway B, Centeno J, Finkelman R, Fuge R, Lindh U, Smedley P, 2005 “Essentials of Medical Geology” Elsevier Academic Press. Siegel F R, 2002 “Environmental Geochemistry of Potentially toxic Metals” Springer Verlag. Smedley P L and Kinniburgh D G, 2002 “A review of the source, behavior and distribution of arsenic in natural waters” Applied Geochemistry 17:517 Smith A, 2002 “As Epidemiology and drinking water standards” Science 296:2145-1246.
Sracek O, Bhattacharya P, Jacks G, Gustafsson J, Bromssen M, 2004 “Behavior of arsenic and geochemical modeling of arsenic enrichment in aqueous environments” Applied Geochemistry, 19:169. USGS, 1999 “Arsenic, Nitrate, and Chloride in Groundwater, Oakland County, Michigan” Fact sheet 135-98 US Department of the Interior. Waugh A E, 1952 “Elements of Statistical Methods” Mc Graw Hill Co 3rd ed.
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Webster and Nordstrom D K, 2003 “Geothermal Arsenic, Chapter 4” in Welch A H and Stollenwerk K G, 2003 “Arsenic in Ground Water”, Kluger Academic Publishers Welch A H and Stollenwerk K G, 2003 “Arsenic in Ground Water”, Kluger Academic Publishers.
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Appendix A.
Sampled Data of Juarez City and Suburbs
Arsenic and parameters measured in Juarez in the Hueco basin from March to December 2004. (NC: not collected)
well# As (aq) ppb
pH Temperature C EC (uS/m) DO ppm elevation ft latitude N longitude W (-)
MXS43I 0.9 7.67 18.6 667 6.66 4753 31.699 106.564 MXS001 86.6 8.8 33.4 2313 0.53 4003 31.43 106.113 MXS002 5 7.8 23.3 1152 5.53 3633 31.329 106.055 MXP003 4.9 7.8 23.3 1153 5.53 3633 31.329 106.045 MXP004 9.8 7.79 24.7 1093 6.65 3634 31.329 106.045 MXS005 70.2 8.74 35.7 1807 0.05 3620 31.459 106.193 JMAS 110 10.3 NC NC NC NC NC NC NC JMAS 113 6.5 7.79 20.7 1.431 NC NC NC NC JMAS 115 10.5 NC NC NC NC NC NC NC JMAS 115 R 1.9 NC NC NC NC NC NC NC JMAS 117 10.5 NC NC NC NC NC NC NC JMAS 119 4 7.71 15.1 1290.5 NC NC NC NC JMAS 11R 11.4 8.18 22.3 1031 NC NC NC NC JMAS 12 1.9 NC NC NC NC NC NC NC JMAS 121 17.4 8.06 21.6 1055 NC NC NC NC JMAS 130 22.2 NC NC NC NC NC NC NC JMAS 133 6.6 NC NC NC NC NC NC NC JMAS 134 18.1 NC NC NC NC NC NC NC JMAS 136 R 6.1 NC NC NC NC NC NC NC JMAS 138 5.9 NC NC NC NC NC NC NC JMAS 13RR 1.7 7.66 21.1 2410.5 NC NC NC NC JMAS 145 23.3 NC NC NC NC NC NC NC JMAS 149 7 8.36 24.2 314.9 NC NC NC NC JMAS 15 R 1.9 NC NC NC NC NC NC NC JMAS 150 6.9 8.2 22.3 708 NC NC NC NC JMAS 160 8 8.3 24.6 672 NC NC NC NC JMAS 160T 7.8 NC NC NC NC NC NC NC JMAS 161 6.1 NC NC NC NC NC NC NC JMAS 163 14.2 NC NC NC NC NC NC NC JMAS 171 7.4 8.26 23.5 541 NC NC NC NC JMAS 17R 10.4 7.86 23.9 992 0.2 3795 31.752 106.425 JMAS 182 5.8 8.34 21.5 742 NC NC NC NC JMAS 183 7.5 NC NC NC NC NC NC NC JMAS 187 8.7 NC NC NC NC NC NC NC JMAS 188 7 NC NC NC NC NC NC NC JMAS 19 R 14 NC NC NC NC NC NC NC JMAS 191 8.9 8.48 25.6 609 NC NC NC NC JMAS 193 9.3 NC NC NC NC NC NC NC JMAS 198 11.3 NC NC NC NC NC NC NC JMAS 202 7.5 NC NC NC NC NC NC NC JMAS 205 8.6 NC NC NC NC NC NC NC JMAS 212 15.3 NC NC NC NC NC NC NC JMAS 218 15.7 NC NC NC NC NC NC NC
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well# As (aq) ppb
pH Temperature C EC (uS/m) DO ppm elevation ft latitude N longitude W (-)
JMAS 219 8 NC NC NC NC NC NC NC JMAS 222 7.2 NC NC NC NC NC NC NC JMAS 23 3.1 NC NC NC NC NC NC NC JMAS 33 2.2 7.93 19.7 1066.5 NC NC NC NC JMAS 42 R 5.9 NC NC NC NC NC NC NC JMAS 47 R 15.6 NC NC NC NC NC NC NC JMAS 48 4.9 NC NC NC NC NC NC NC JMAS 49 3.2 NC NC NC NC NC NC NC JMAS 5 B 3.1 NC NC NC NC NC NC NC JMAS 58 3.5 8.12 21.1 1011 NC NC NC NC JMAS 5Z 6 NC NC NC NC NC NC NC JMAS 62 2.8 7.66 20.5 1604 NC 3724 31.75 106.434 JMAS 63 R 3.3 NC NC NC NC NC NC NC JMAS 67 R 7 NC NC NC NC NC NC NC JMAS 72R 4.4 8.11 22.3 573 NC NC NC NC JMAS 76 6.4 NC NC NC NC NC NC NC JMAS 8 Za 13.2 NC NC NC NC NC NC NC JMAS 84 3.1 8.13 21.1 731.5 NC NC NC NC JMAS 88 5.3 NC NC NC NC NC NC NC JMAS 94 R 5.2 NC NC NC NC NC NC NC JMAS 95 6.8 8.13 24.3 537 NC NC NC NC JMAS 98R 5 8.07 22.8 676 NC NC NC NC JMAS 99R 5.5 8.53 23.9 552 NC NC NC NC JMAS 9R 3.6 7.66 20.7 1876 0.18 NC NC NC JMAS 9R 3.7 NC NC NC NC NC NC NC JMAS R 1 11.7 NC NC NC NC NC NC NC JMAS Z4 8.6 NC NC NC NC NC NC NC
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Appendix B.
Detailed information of EPWU wells # 9, 305 and 306
In this appendix the detailed data available for the three wells with ample information is
presented. From these wells cuttings from two or three different depths were analyzed and
leached in contrast to one sample for the rest of the cuttings, as discussed in Chapter 4.
The wells 305, 306 and 9 were sampled for cutting and leaching experiments in two or three
different depths because more information of them was available: either speciation data (for
wells 305 and 306 in the Canutillo Field in the Mesilla basin) or in the well# 9 case, because its
sediments have the darkest color, indicating either high iron or high organic matter content
(Figure 1). No speciation analyses were found in the archives for any well in the Hueco basin.
Well# 9: Has arsenic mean 15.5 ppb, screen depth ranging from 462 to 960 ft. Well 9 is artesian
and has the cuttings with the darkest color (Figure 1), this color characteristic indicates either
high iron content (Table 1a Chapter 4), and their measurements along this well may help to
understand the arsenic mobilization in it. Two different depth cuttings were chosen, one at 290 ft
that is shallow depth than the screen, and probably before the confinement layer is. The second
sample depth in the confined aquifer (540 ft depth) from where the high arsenic is registered in
archives. Cuttings results showed that Iron in solid phase was the highest with values of 19400
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and 19700 ppm (Table 1). In well 9 all of As, Fe and TOC, contents in solid phases, increased
with depth; in other hand more arsenic was leached from the material at shallow depth for pH 9
and the same proportion of arsenic was leached at ph 10 for both depths.
Well# 305: Has the highest arsenic observation 95 ppb in the entire EPWU archive on 1985. It is
in the intermediate aquifer in the Mesilla basin, and speciation analyses exist for it. Two different
sample depths were analyzed: 100 ft, before the confinement layer lies, and 330 feet in the
confined aquifer. Screen range between 220 and 400 ft. Arsenic average in well 305 (not
considering the 95 ppb observation) is 18.5 ppb. (up to 24.4 ppb considering the 95 ppb). Table 1
shows that
Well# 306 has 43% of AsIII % (percentage of the total arsenic in water) and cuttings were
available also. The well# 307 with the highest AsIII % up to 62% was not in the cuttings
archives. Reductive dissolution is a mechanism that was tested in this work in well 306 cutting
material, and three different sample depths were analyzed: 100 ft, before the confinement starts,
330 and 490 feet in the confined aquifer. The screen in this well is between 230 and 460 feet
depth. The main clayey aquitard layer is above 230 feet, the water this well yields has mean As =
12.6 ppb. Canutillo wells in general have not a confinement sharply bordered but the aquitard is
clay interbeded with sand and gravel.
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Table B1. Speciation analyses of wells# 305 and 306 in the intermediate aquifer in the Mesilla basin (Canutillo field thru the Rio Grande alluvium to the alluvial fan facies, west to the Franklin Mts.)
EPWU 2001. well date
colected ph C (temp) Astot ug/L
As_solv ug/L
AsIII ug/L
AsIII %tot
AsV ug/L
AsV %tot
305 12/8/2000 8.1 22.3 22 21.9 4.3 19.5 17.7 80.5 305 12/14/2000 8 23.3 20.9 16.1 0.8 3.9 20.1 96.1 305 12/20/2000 8 22 23.7 21.2 6.2 26.2 17.5 73.8 305 12/28/2000 7.9 19.1 20.1 20 5.1 25.3 15 74.7 305 12/29/2000 7.5 19.7 20.5 15.4 4.1 20.2 16.4 79.8 305 1/4/2001 8.2 18.7 22.2 21.8 3.5 16 18.6 84
305 averages 7.95 20.85 21.57 19.40 4.00 18.52 17.55 81.48
well date colected ph C (temp)
Astot ug/L
As_solv ug/L
AsIII ug/L
AsIII %tot
AsV ug/L
AsV %tot
306 12/8/2000 7.9 24.2 17 17.4 6.6 38.9 10.4 61.1 306 12/14/2000 7.6 24.1 12.9 13.1 3.7 28.4 9.2 71.6 306 12/20/2000 8 22 17.7 17.5 7.6 43.1 10.1 56.9 306 12/29/2000 7.9 19.7 16.7 19.2 4 23.8 12.7 76.2 306 1/4/2001 7.8 21.2 16.8 16.2 3.9 23.1 12.9 76.9
306 averages 7.84 22.24 16.22 16.68 5.16 31.46 11.06 68.54
Figure B 1. Well # 9 with highest Fe content shows dark color clays (sample from 290’ deep on left, sample from 540’ in center). Well # 112 (right), as the rest of the samples, has clear sand color.
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Curriculum Vitae
Elia Margarita Marquez and Humberto Benitez gave life to Elia. She married her quantum mechanics teacher Ricardo Vazquez; and studied elementary school, high school, bachelor in Physical Engineering all in the Mexico City. She also was teacher of applied Mathematics and Physics to Engineering, Industrial Design, Architectural, and to Economy students in the Autonomous Metropolitan University in Azcapotzalco, Mexico. Elia B Marquez received her Ph. D. in Environmental Science and Engineering as well as her M.S. degree in Physics from the University of Texas at El Paso. Elia acknowledge and thank immensely to the United States of America for the great opportunity to study at the University of Texas at El Paso.
Her publications and congress presentations include: Marquez E, Gurian P. L, Goodell P, Barud-Zubillaga A “Aspects of Arsenic Concentration in the El Paso Region Groundwater”, The Hueco Bolson/Rio Grande Aquifer Meeting III, El Paso TX. 2005; Marquez E, Gurian P, Goodell P, Barud-Zubillaga A “Statistical Study of groundwater Arsenic in El Paso TX”, NMSU-WRRI Meeting, Socorro NM, August 2005; Marquez E, Gurian P. L, Goodell P, Barud-Zubillaga A “Arsenic Geochemistry in the El Paso region” Naturally Occurring Contaminants 2005 Conference of the National Groundwater Association, February 2005, Charleston SC; Benitez-Marquez E, B Diaz, and P L Gurian, 2004 “Understanding the Associations Between Statewide Diabetes Prevalence and Air Pollution Emissions,” Diabetes Care, 27 (6):1515-7; Lopez R, Benitez-Marquez E, Wiltberger M, Lyon J “Evidence for quasi-steady near Earth Magnetotail Reconnection during Magnetic Storms using Global MHD simulation results and Magnetotail Magnetic Field Observations”, Adv. Space Res. Vol. 31, No 5, 2003; Marquez E, Lopez R “About Magnetic Reconnection in the Earth” Presentation in the Texas section of the APS in Houston TX, 2000; Marquez E, Gurian P, Goodell P, Barud-Zubillaga A “Statistical Study of the Arsenic in the Groundwater”, poster at the UTEP Graduate Students Association (GSA) meeting on April 2004 and also the actualization of results in a poster at the UTEP Model Institutions for Excellence (MIE) meeting on 2005, and also as part of a team she has published in the Diabetes Care Journal on 2004, part of her dissertation work. Dr. Marquez’ dissertation, “Identifying Statistical Trends for Environmental Quality Based on Archival Convenience Databases”, was directed by Dr. Patrick L. Gurian professor in Drexel University, PA. Elia has not permanent address now.
