Arsenic concentrations in shallow groundwater of Araihazar, Bangladesh: Part II

Transcription

1 Arsenic concentrations in shallow groundwater of Araihazar, Bangladesh: Part II Hydrologic control reflected in the electromagnetic conductivity of soils Z. Aziz 1,2, A. van Geen 2 *, R. Versteeg 3, A. Horneman 4, Y. Zheng 2,5, S. Goodbred 6, M. Steckler 2, M. Stute 2,7, B. Weinman 6, I. Gavrieli 8, M.A. Hoque 9, M. Shamsudduha 9, and K.M. Ahmed 9. 1 Department of Earth and Environmental Sciences, Columbia University, NY, USA 2 Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA 3 Idaho National Laboratory, Idaho Falls, Idaho, USA 4 Department of Earth and Environmental Engineering, Columbia University, NY, USA 5 Queens College, City University of New York, Flushing, NY, USA 6 Earth and Environmental Sciences, Vanderbilt University, Nashville, TN, USA 7 Barnard College, New York, NY, USA 8 Geological Survey of Israel, Jerusalem, Israel 9 Department of Geology, University of Dhaka, Dhaka, Bangladesh Submitted to Water Resources Research, January 9 th, 2006 *Corresponding author Phone: (845) ; Fax: (845) avangeen@ldeo.columbia.edu 1

2 Abstract Groundwater As concentrations measured in 5200 wells <22 m (75 ft) deep across a 25 km 2 area of Bangladesh are compared with variations in the nature of surface soils, as determined at 18,500 locations by frequency-domain electromagnetic induction. Auger cores from the area indicate that a combination of grain size and the conductivity of soil water dominate the electromagnetic signal. A simple linear regression model of groundwater As as a function of well depth and electromagnetic conductivity accounts for a significant fraction of the variance for paired measurements within 50 m of each other (r 2 =0.30, n=512). The spatial patterns suggest that groundwater recharge throughout permeable sandy soils prevents groundwater As concentrations from rising in shallow reducing aquifers. 2

3 1. Introduction It took a small group of dedicated scientists a decade to convince the world that elevated As concentrations in groundwater pumped from millions of wells across the Ganges-Brahmaputra- Meghna (GBM) delta poses a serious health threat (Chakraborty and Saha, 1987; Dhar et al., 1997). After another decade of intense field and laboratory research conducted by many more researchers, the processes that lead to elevated As concentration in groundwater of the region remain poorly understood. One reason for the limited progress to date is undoubtedly the extreme degree of spatial variability of the geology of a large fluvio-deltaic system such as the GBM (BGS/DPHE, 2001; Yu et al., 2003). This variability has made it difficult to generalize detailed observations of groundwater and aquifer solids obtained from a limited number of sites to a broader region. Aside from such geological, and therefore hydrological, complexities, the origin of the electron donors that presumably drive As mobilization in reducing sandy aquifers remains unclear, with some scientists favoring an advective source of dissolved organic matter (associated with either surface recharge or peat layers) over a particulate source of organic matter incorporated with sandy aquifer material at the time of deposition (Harvey et al., 2002; McArthur et al., 2004). To complicate matters further, the frequently invoked link between microbiallymediated Fe reduction and As mobilization has recently been questioned (Horneman et al., 2004; van Geen et al., 2004; Polizzotto et al., 2005). Despite the difficulties, the interaction of potentially multiple factors must urgently be understood as it is likely to determine the sustainability of the numerous aquifers in the region that are presently low in As and offer the best hope of reducing the exposure of the population to As in the short term (van Geen et al., 2002; van Geen et al., 2003a; Zheng et al., 2005). 3

4 In an attempt to decompose the groundwater As problem into more tractable questions, the present study focuses on the processes that regulate As concentrations in shallow aquifers of relatively recent (Holocene) age. The field observations extend over a 25 km 2 area of Araihazar upazila where the distribution of As in the groundwater has been mapped at unprecedented resolution by sampling of thousands of tube wells (van Geen et al., 2003b). To our knowledge for the first time in Bangladesh, we apply here a geophysical surveying method, frequencydomain electromagnetic induction, to relate spatial variations in the nature of surface soils properties to As concentrations in shallow groundwater. When considering only shallow wells, defined here as wells <22 m (75 ft) deep (metric and imperial units of depth are listed because the latter are widely used in Bangladesh), Araihazar marks a transition zone between the coastal region to the south, where very few wells meet the 50 µg/l Bangladesh standard for As in the drinking water, and inland areas to the north where most shallow wells meet the 10 µg/l WHO guideline for drinking water As (Fig. 1). Within Araihazar, and throughout much of Bangladesh, As concentrations in groundwater span a particularly wide dynamic range of < µg/l at 22 m (75 ft), starting from a much narrower range of <1-100 µg/l in the shallowest wells that extend from 8 to 12 m depth (BGS/DPHE, 2001; van Geen et al., 2003b). Frequency-domain electromagnetic (EM) induction techniques have been applied in hydrology to map percolation of water in the vadose zone, estimate the extent and internal structure of shallow aquifers, and determine the extent of groundwater contamination (McNeill, 1980a and 1990; Cook et al., 1989; Pellerin, 2002; Pettersson and Nobes, 2003). A number of studies have shown that hand-held EM instruments deployed by walking through cultivated fields can also be used to determine the spatial variability of soil salinity (McNeill, 1986; Hendrickx et.al., 1992; 4

5 Lesch et al., 1995; Vaughan et al. 1995; Doolittle et.al., 2001; Triantafilis et al. 2002). It has, however, typically been difficult to disentangle the direct contribution of fine-grained particles to EM conductivity from an indirect contribution due to the accumulation of salt by evaporation at the surface of relatively impermeable deposits (Williams and Hoey, 1987; Cook et al., 1989; 1992; Doolittle et al., 1994; Humphreys et al., 1995, Johnson et al., 2001, Triantafilis et al., 2002). In addition to salt content and grain-size, some studies have shown that variations in soil moisture can influence the EM conductivity signal (e.g Waine et. al., 2000) whereas, in a different setting, Banton (1997) observed little difference in EM response between wet and dry conditions. In this paper, we compare the spatial distribution of over 18,000 EM induction measurements in open fields of Araihazar with As concentrations in groundwater sampled from 5200 shallow wells (<22 m or <75ft deep) in the same area. Section 2 of this paper describes the geological setting of the study and the spatial distribution of arsenic in shallow groundwater. Section 3 reviews the principles of EM induction and describes how the surveys were conducted. Collection and analyses of a dozen soil profiles obtained with a hand auger to calibrate the EM response are also described. In Section 4, the general pattern of EM variations in the study area is described and compared to the soil profiles. Section 5 leads to a simple linear regression model of shallow groundwater As as a function of both depth and EM conductivity. Section 6 offers an explanation for the observed patterns that draws from a recent demonstration by Stute et al. (in revision) of the importance of the permeability surface cover in regulating the recharge rate, and therefore the As content, of shallow Bangladesh aquifers. 5

6 2. Geological Setting Erosion of mountains surrounding the GBM delta has deposited thick layers of clay, silt, sand, and gravel across much of Bangladesh over the past 7 kyr of the Holocene (Morgan and McIntire, 1959; Umitsu, 1993; Goodbred and Kuehl, 2000; BGS and DPHE, 2001; Goodbred et. al., 2003). Araihazar straddles a transitional region that extends from the uplifted Madhupur Tract to the northwest, where only surface soils contain recently deposited material, to the active floodplain of the Meghna River to the southeast where up to 150 m of Holocene sediment accumulated during the Holocene (van Geen et al., 2003b; Zheng et al., in press). The Old Brahmaputra River running through the study area (Fig. 2) today is a modest stream that stops flowing during the dry season, but there are indications of a more powerful stream passing through the area that may have shifted its course in the 18 th century along with the main channel of the Brahmaputra River (Fergusson, 1863; Brammer, 1996; Weinman et al., in review). Procedures followed to collected groundwater from largely private wells in Araihazar and to analyze the well water for As are described in detail by van Geen et al. (2003b; 2005). Well depths are estimated on the basis of the owner s recollection of the number PVC pipes used in the construction of his well. Measurement of the actual depths of a limited number of wells (16 wells) indicates that this information is generally accurate within 1-6 m (10-20 ft). Nearly 80% of the totals of 6615 wells inventoried in the 25 km2 study area are up to 22 m (75 ft) deep. Almost two-thirds of these shallow wells contain <50 µg/l As; 17% meet the WHO guideline of 10 µg/l. Geographically, many of the low-as wells are concentrated in two parallel strings of villages that run diagonally across the study area (Fig. 2a). The density of wells within the m (45-55 ft) depth range is roughly four times greater than in contiguous 8-m (25 ft) intervals 6

7 above and below. From 6 to 18 m (20-60 ft) depth, the proportion of wells containing >50 µg/l As increases steadily from ~20 to 80% (van Geen et al.,2003b). The highest groundwater As concentrations, approaching 1000 µg/l, are most frequently observed between 12 and 18 m (45-55 ft) depth (Fig. 3). 3. Methods 3.1 EM Conductivity Survey The geophysical survey of the study area was conducted with a Geonics EM31 instrument (McNeill, 1980a, 1990). The EM31 consists of a transmitter coil radiating an electromagnetic field at 9.8 khz and a receiver coil located at opposite ends of a 3.7-m long boom. By inducing an eddy current in the ground, the primary field generates a secondary electromagnetic field that is recorded by the receiving coil. The intensity of the secondary field increases with the conductivity of the ground. This conductivity is a function of the concentration of ions dissolved in soil water as well as exchangeable ions at the solid/liquid interface. The secondary field generated below the soil surface diminishes with depth. The penetration of the signal depends on the separation between the transmitting and receiving coils, the transmission frequency, and the coil orientation (McNeil, 1980a). When the boom is held at waist height horizontally, which corresponds to a vertical dipole orientation, 50% of the signal is generated in the upper 90 cm of the soil. The conductivity of layers below 180 cm depth still accounts for 27% of the signal (McNeil, 1980a; Doolittle, 2001). Nearly 18,500 EM31 readings were collected in Araihazar, primarily during the dry months of January 2001, January 2002, and March-June Flooding precluded data collection during 7

8 the wet season. At the beginning of each day, the instrument was calibrated following the standard procedure described in the operating manual. The distance between sequential measurements along a particular transect ranged from 5 to 10 wide steps, i.e. ~4 to 8 m. The reproducibility of readings at a single location was generally within 0.5 ms/m. Each determination, rounded to the nearest unit of ms/m, was entered by hand in the field on a Compaq Pocket PC connected to a Global Positioning System (GPS) receiver using ESRI ArcPad software. EM conductivity measured along the edge of flooded rice fields was typically ~1 ms/m higher than over adjacent dry areas. Coverage is limited to open areas to avoid interference from corrugated iron plates used in the villages for building and fences. EM data were also not collected within m of overhead power lines. 3.2 Collection and Analysis of Soil Samples At 14 locations spanning the spectrum of EM conductivity values in Araihazar (Fig. 2), soil samples were collected to a depth of 200 cm at 25 cm intervals with a hand-auger. For each horizon, the particle size distribution and the electrical conductivity of sediment slurry were determined. Samples were disaggregated in distilled water and washed through a 63 µm sieve to determine the sand fraction. A Micromeritics SediGraph 5100 hydrometer was used to quantify the silt and clay fraction down to 0.8 µm. For slurry conductivity measurements, wet soil (~2 g) was diluted in 15 ml of deionized high-purity water delivered by Milli Q system and allowed to equilibrate overnight. An Orion 105Aplus conductivity meter with a MD conductivity probe, calibrated with a 1413 us standard solution (Orion ), was then used to measure the conductivity of the slurries. The water content of soil samples, tightly packed in plastic 8

9 containers at the time of collection, was measured by drying in an oven at 65 o C for 24 hours and ranged from 0.5 % by weight in the case of sands to 30% in clays. 4. Results 4.1 Distribution of EM conductivity EM conductivities measured in open areas of Araihazar span a wide range of 3 to 215 ms/m and average 18+8 ms/m (n=18,530). The histogram of EM conductivity, with a median of 17 ms/m and a variance of 56 ms/m, is slightly skewed towards higher values (Fig. 4). Nearly 99% of the measurements are in the ms/m range, however. The largest region where elevated conductivities (30-50 ms/m) were consistently recorded is in the northwestern part of the study area (Fig. 2b). There are also two smaller patches of elevated conductivities ~1.5 km to the northeast and south of this region, respectively. A large km wide swath of moderately high EM conductivities (20-30 ms/m) runs north-south through the eastern portion of the study area. This is also a region where the As content of the vast majority of wells <22 m (75 ft) deep exceeds 100 µg/l (Fig. 2a). At the other end of the spectrum of conductivities, a well defined swath of low conductivity (<13 ms/m) runs diagonally along the northwestern boundary of the study area (Fig. 2b). Several villages located within this region are populated with shallow wells that are consistently low in As, but there are also patches within this area containing wells that are predominantly elevated in As (Fig. 2a). The southern diagonal alignment of villages with wells predominantly low in As also appears to be associated with generally low conductivities in surrounding fields. 9

10 4.2 Profiles of Soil Properties Conductivity of Soil Slurries EM conductivities range from 3 to 35 ms/m at the 14 sites where auger cores were collected (Fig. 2b). The conductivity of slurries of the underlying soil measured with a conductivity probe, referred to hereon as slurry conductivity, range from 5 to 60 ms/m after dilution. For ease of viewing, the profiles of slurry conductivity are subdivided into three groups. Slurry conductivities average ms/m (n=112) and are essentially uniform with depth for the 6 profiles in the lowest category of EM conductivities (3-10 ms/m; Fig. 5a). This is also the case for 1 out of 4 profiles from sites in the intermediate EM conductivity range (11-15 ms/m; Fig. 5b). The other 3 profiles in this category, however, show 2 to 6-fold increases in slurry conductivity in the deepest sample at 200 cm. A similar increase in conductivity at depth is observed for 3 out of 4 profiles in the highest category of EM conductivities (20-35 ms/m; Fig. 5c). In this category, slurry conductivities in the upper 150 cm of the soil are also higher (14+8 ms/m, n=14) than for the two groups of sites with lower EM conductivities Grain-size The nature of the soils sampled with the hand auger ranged from thick layers of sticky clay to deposits of coarse sand similar to aquifer material from which groundwater is extracted at greater depths. Although the contribution of intermediate-sized silts also varied significantly (2-72 %), the grain-size data can be conveniently summarized on the basis of profiles of the clay (< 2 µm) fraction only. One justification is that the sand and silt fraction of soils are poor conductors (McNeill, 1980b). Soil conductivity is therefore largely determined by the proportion of clay. With the exception of a few horizons, the clay content of soils in the groups of profiles with low 10

11 and intermediate EM conductivities ranges from 1 to 20% (Fig. 6a,b). The clay content is <5 % in 21 out of 48 samples in the low EM category but in only 6 out of 32 samples in the intermediate category. In contrast, the clay content of all samples collected from sites in the highest EM category is always >10% and frequently exceeds 50 % (Fig. 6c). At the site where the highest EM conductivity of 35 ms/m was measured, the clay of the soil in 7 out of 8 samples is >60%. The core collected from the site with the lowest EM value within the high EM category (20 ms/m) is also the only one that contained some silty sand. The correlation between slurry conductivity and clay fraction (r 2 =0.34, n=112) is poor primarily because slurry conductivity is highest at depth whereas the clay fraction is highest towards the surface for the profiles within the highest EM category. 5. Discussion 5.1 EM Conductivity and Sediments Properties From first principles, McNeil (1980a) showed that the contribution from different depths to EM conductivity can be calculated as S A = S 1 [1 - R V (Z 1 )] + S 2 [R V (Z 1 ) - R V (Z 2 )] S N R V (Z N ), 4Z where S 1 S N are soil conductivities at depths Z 1 Z N, and R V ( Z) = is the 2 3 / 2 (4Z + 1) cumulative response function specifying the contribution from each depth, expressed as a fraction of the distance separating the emitting and receiving coils. We make here no attempt to calibrate the EM31 signal in any absolute sense, nor do we try to separate the contribution to EM conductivity of ions present in soil water and exchangeable ions at the solid/solution interface (Cook et al., 1989). Instead, we compute an integrated slurry conductivity and an integrated clay fraction from different soil layers by applying the formulation of McNeil (1980a) separately to the measured slurry conductivities and to the clay fractions at every auger site. EM conductivity 11

12 measured over the 14 auger sites broadly increases with the integrated slurry conductivity (Fig. 7a). But EM conductivities also increase fairly systematically as a function of the integrated proportion of clay in the soil (Fig. 7b). Linear regression shows that integrated slurry conductivity (r 2 =0.72, n=14) and integrated clay fraction (r 2 =0.83, n=14) correlate well with EM conductivities measured at the surface, and this despite the fact that a significant portion of the EM signal is determined by layers beyond a depth of 2 m. It is difficult to determine if the clay fraction or the conductivity of soil water dominate the EM signal. But in a setting similar to Bangladesh, Kitchen et. al. (1996) observed strongly reduced EM conductivity over sand deposits dating from the 1993 flood of the Missouri River compared to surrounding finer soils. Doolittle et al. (2002) also located sub-surface sand blows in southeastern Missouri by EM conductivity. In Australia and in the US, EM conductivity surveys have been used to optimize rice production by identifying areas where irrigation water is lost to infiltration through sandy deposits (Beecher et.al, 2002 and Anderson-Cook et.al., 2002). In our study area of Araihazar, comparison with auger cores clear indicate that the EM conductivity survey produced an extensive map of a property that is closely related to the clay content, and therefore permeability, of surface soils. 5.2 Geostatistics of EM Conductivity Distribution The experimental variogram is a convenient graphical representation of the continuity, or roughness, of spatial data (Cressie, 1993; Robertson, 2000; Webster and Oliver, 2001). It is 1 2 calculated using the equationγ ( h) = ( Z i Z i + h ) where γ(h) is semivariance for 2N( h) 12

13 interval distance class h, Z i is measured sample value at a point h, Z i+h is measured sample value at a point i+h and N(h) is the total number of sample couples for the lag interval h. To obtain the variogram of EM conductivity in Araihazar, distances up to 1000 m separating two measurements were considered and binned in 10-m intervals. The results show that the variance of pairs of EM measurements increases steadily from 0.1 (ms/m) 2 for sites 10 m apart to ~55 (ms/m) 2 at a separation distance of 400 m (Fig. 8). This quantitatively confirms our field experience that a spatial resolution of ~10 m is generally sufficient to capture variations in EM conductivity >1 ms/m. The increase in variance between pairs of determinations up to a distance of 400 m is consistent with the ~500 m scale of several areas of consistently high and low conductivity distributed across the study area (Fig. 2b). An exponential model was fitted to the variogram by minimizing the sum of squared residuals (RSS = 2266; r 2 = 0.825) between the model and the observations (Fig. 8). The model was used to contour the EM conductivity data and visually enhance the main features of the survey by kriging (Fig. 10a). Kriging estimates optimally-weighted averages for unsampled locations of an irregularly spaced data set (Cressie, 1993, Robertson, 2000). In a cross-validation, each data point was removed from the set in turn and its value was estimated by kriging (Robertson, 2000). A comparison of estimated and actual EM conductivities (cross-validation) shows that kriging adequately captures the main features of the variability in soil composition, with a standard error of predictions EM of ~2 ms/m (Fig. 9). 5.3 Relation between EM conductivity and As concentrations in groundwater The contour map facilitates visual comparison of EM conductivity with the distribution of the As in shallow wells because the two data sets typically do not overlap. The merging of the two data sets shows, for instance, that interruptions of the diagonal swath of mostly low-as wells across 13

14 the northwestern portion of the study area by clusters of high-as wells occur precisely in areas of elevated EM conductivity (Fig. 10b). Similarly, two clusters of generally low As concentrations to the south are closely delimited by areas of elevated EM conductivity. Overall, the average As content of shallow wells located within the 10 ms/m contour is 43±67 µg/l (n=606) whereas the average As content of wells located outside the same contour is 120±123 µg/l (n=2199). Another way to express the contrast between the two areas is that 73% of shallow wells located within the 10 ms/m contour contain <50 µg/l As, whereas this is the case for only 36% of wells outside the same contour. For a more rigorous comparison between groundwater As and EM conductivity data, we return to the actual EM data instead of the extrapolated values. For each shallow well, the distance to the closet EM measurement was computed using Arcview GIS 3.2. To obtain a general sense of the relation between EM conductivity and As concentration, a box plot was constructed considering pairs of data separated by 50m. A striking feature is that average As concentrations steadily rise as a function of EM, but only up to a conductivity of 25 ms/m (Fig.11). We have no explanation for the breakdown of the relation at higher EM conductivities, which encompass 15% of the 616 paired measurements up to 50 m apart. Further regression analysis confined to EM conductivities <25 ms/m indicates that depth must be taken into account when attempting to relate to the distribution of As in shallow wells. For this exercise, pairs EM conductivity and nearest well As measurements were divided in 50-m bins. Univariate regressions of groundwater As concentrations as a function of EM conductivity for each of these distance intervals indicates a significant but scattered relationships between the 14

15 two properties that weakens with increasing distance from r 2 = 0.13 for pairs <50 m (n=518) apart to r 2 = 0.02 for pairs >400 m apart (n=1318) (Table 1a). Univariate regression of As as a function of depth already accounts for a larger variance of the data, with an r 2 =0.22 (n=518) for wells with 50 m of the nearest EM measurement (Table 1b). Serendipitously, the correlations of As as function of depth also weaken as the distance between pairs of measurements increases. When both EM conductivity and depth are considered simultaneously, nearly one third of the variance of As concentrations in shallow wells is accounted for pairs of measurements <50 m apart (Table 1c). A comparison of measured As concentrations with predictions from this simple linear model visualizes the scattered but clearly significant relationship (Fig. 12). Substituting different EM conductivities in the regression suggests that the permeability of surface soils affects the intercept rather than the slope of the local As-depth relationship (Fig. 3). 5.4 Implications for the regulation of groundwater As concentrations In what way could the permeability of surface soils influence As concentrations in shallow aquifers? Stute et al. (in revision) recently reported profiles of groundwater age based on the 3 H- 3 He dating method for 6 nests of monitoring wells distributed across the Araihazar study area. The data show that the time elapsed since recharge increased steadily with depth at all sites at a rate that varied considerably from one nest of wells to the other. At the two sites located within an area of low EM conductivity (Sites C and F; Fig. 10b), the age of groundwater interpolated to a depth of 12 m (40 ft) was 3 and 4 years, respectively, implying an average recharge rates of ~0.6 m/yr. In contrast, at two sites surrounded by fields of high EM conductivity (Sites A and B; Fig. 10b) the recharge rates calculated from the age of groundwater (18 and 19 years, respectively) interpolated to the same depth average only 0.08 m/year (groundwater ages could 15

16 not be determined at the 2 remaining sites; see Stute et al., in revision). Considered in conjunction with the distribution of the clay fraction size inferred from EM data, these observations indicate that sandy aquifers that essentially extend to the surface are recharged much more rapidly than aquifers that are capped by a relatively impermeable layer of finegrained sediments. Previous studies have also associated elevated EM conductivities measured at the soil surface with low recharge, and vice-versa (Cook et.al, 1989; 1992). But to our knowledge, the combination of isotopic and geophysical data presented here provides the first quantitative demonstration of an intimate connection between the patchy distribution of grainsize of shallow sediments in a deltaic floodplain and the rate of local recharge. Stute et al. (in review) also report that As concentrations in shallow aquifers of Araihazar increase roughly linearly with the age of the groundwater since recharge, as determined by the 3 H- 3 He dating method. This remarkable relationship, constrained by a total of 13 data points from 6 nests of monitoring wells for which groundwater age could be calculated unambiguously, indicates that aquifer material releases As to groundwater at a relatively constant rate of ~20 µg/l per year across a wide range of conditions. The underlying mechanism of this constant rate is presently not understood, but there are clear implications for the present study. The relatively constant rate of As release to groundwater that was documented provides the necessary linkage between, on one hand the apparent control of groundwater age in shallow aquifers by the permeability of surface soils, and on the other the observation that groundwater As concentrations are elevated in aquifers capped by fine-grained sediment. 16

17 5.5 Relationship between shallow groundwater As and surface geomorphology In a companion study, Weinman et al. (in review) document the geological history that resulted in the present geomorphology of Araihazar. Grain-size analysis of a considerably larger number of auger cores indicates that low-em conductivity areas of Araihazar are typically associated with high energy, sandy depositional environments such as relict channel levees and bars. In contrast, the geology of several of the very high-em conductivity areas clearly indicates abandoned channels with overlying fine-grained fill. On the basis of these observations, one can therefore relate the patchiness in the distribution of As in shallow aquifers not only to the variability of local recharge but also to the complex depositional history that sets up the spatial distribution of local recharge. As Weinman et al. (in review) point out, such relationships makes one wonder about the possible impact on groundwater As concentrations in shallow wells at the local scale of human activities that alter the landscape, such as raising of land to protect villages from flooding, digging of ponds, and the water works associated with irrigation for rice cultivation. The relationship observed between groundwater As and the permeability of surface soils at the ~100-m scale also naturally leads one to speculate about the implications on larger scales. It is well established that surface deposits in northern Bangladesh are generally coarser than in southern Bangladesh (Goodbred et al, 2003). The associations observed in an intermediate environment such as Araihazar suggest surface geomorphology, and its impact on the rate of local recharge, could be an important factor contributing to generally low As concentrations in shallow wells of northern Bangladesh and much higher levels towards the coast (Fig.1). 17

18 6. Conclusions The geophysical data presented in this study have allowed us to establish a connection between the nature of surface soils and the distribution As in shallow aquifers of Araihazar. The linkage of elevated groundwater As concentrations with elevated EM conductivity, and vice-versa, could be explained on the basis of two critical pieces of information. First, the connection between EM conductivity and the grain-size established by analysis of a set of auger cores from the same area. Second, the intimate connection between groundwater age and As concentrations that was established independently (Stute et al., in revision). These relationships clearly indicate that the patchiness of As concentrations in shallow aquifers is largely set by hydrological processes reflecting the geological history of floodplain environments (Weinman et al., in review). In order to explain widespread but variable As enrichments in South Asian deltaic aquifers, hydrology must clearly be considered in conjunction with the complex biogeochemical processes that lead to the release of As to groundwater. Acknowledgments Columbia University and the University of Dhaka s research and mitigation work in Araihazar since 2000 has been supported by NIEHS Superfund Basic Research Program grant NIEHS 1 P42 ES10349, Fogarty International Center grant NIEHS 5-D43 TW , NSF grant EAR , NSF grant EAR , and the Earth Institute at Columbia University. 18

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23 McNeill J.D. (1980b) Electrical Conductivity of Soils and Rocks. Technical Note TN-5; Geonics Ltd: Ontario. McNeill J.D. (1986) Rapid, accurate mapping of soil salinity using electromagnetic ground conductivity meters, Technical Note TN-18. Geonics Limited, Ontario, Canada. McNeill J.D. (1990) Use of electromagnetic methods for ground-water studies. In: Ward, S.N. (Ed.), Geotechnical and Environmental Geophysics: I. Review and Tutorial. Society of Exploration Geophysicists, Tulsa, OK, pp Morgan J.P. and McIntire W.C. (1959) Quaternary geology of the Bengal Basin, East Pakistan and India. Bulletin Geological Society of America, 70: Nickson R., McArthur J., Burgess W., Ahmed K.M., Ravenscroft P., Rahman M. (1998) Arsenic poisoning of Bangladesh groundwater. Nature 395(6700), Operating Manual for EM31-D Non-Contacting Terrain Conductivity Meter, Geonics Limited, Pellerin L. (2002) Applications of electrical and electromagnetic methods for environmental and geotechnical investigations. Surveys in Geophysics 23:

24 Pettersson J.K., Nobes D.C. (2003) Environmental geophysics at Scott Base: ground penetrating radar and electromagnetic induction as tools for mapping contaminated ground at Antarctic research bases, Cold Regions Science and Technology 918, 1 9. Polizzotto, M.L., C.H. Harvey, S.R. Sutton, and S. Fendorf (2005) Processes conducive to the release and transport of arsenic into aquifers of Bangladesh, Proceedings of the National Academy of Sciences, Robertson G.P. (2000) GS + : Geostatistics for the environmental science, Gamma Design Software, Michigan, USA. Stute, M., Y. Zheng, P. Schlosser, A. Horneman, R.K. Dhar, M. A. Hoque, A. A. Seddique, M. Shamsudduha, K. M. Ahmed, and A. van Geen, Increase in arsenic concentrations with groundwater age in shallow Bangladesh aquifers, submitted to Water Resources Research, September Triantafilis J., Ahmed M.F., Odeh I.O.A. (2002) Application of a mobile electromagnetic sensing system (MESS) to assess cause and management of soil salinization in an irrigated cottongrowing field, Soil Use and Management 18, Umitsu M. (1993) Late quaternary sedimentary environments and landforms in the ganges delta, Sedimentary Geology 83 (3-4):

25 van Geen A., Ahmed K.M., Seddique A.A., Shamsudduha M. (2003a), Community wells to mitigate the current arsenic crisis in Bangladesh, Bulletin of the World Health Organization, v82, van Geen A., Zheng Y., Versteeg R., Stute M., Horneman A., Dhar R., Steckler M., Gelman A., Small C., Ahsan H., Graziano J., Hussein I., Ahmed K.M.(2003b) Spatial variability of arsenic in 6000 tube wells in a 25 km2 area of Bangladesh, Water Resources Research, 35(5), 1140, doi: /2002wr van Geen A., Cheng Z., Seddique A.A., Hoque M.A., Gelman A., Graziano J.H., Ahsan H., Parvez F., Ahmed K.M. (2005) Reliability of a commercial kit to test groundwater for arsenic in Bangladesh, Environmental Science and Technology, 39(1); van Geen A., Ahsan H, Horneman A.H., Dhar R.K., Zheng Y., Hussain I., Ahmed K.M., Gelman A., Stute M., Simpson H.J., Wallace S., Small C., Parvez F., Slavkovich V., Lolacono N.J., Becker M., Cheng Z., Momotaj H., Shahnewaz M., Seddique A.A., Graziano J.H. (2002) Promotion of well-switching to mitigate the current arsenic crisis in Bangladesh, Bulletin Of The World Health Organization 80 (9): Vaughan P.J., Lesch S.M., Corwin D.L., Cone D.G. (1995) Water content effect on soil salinity prediction: a geostatistical study using cokriging. Soil Science Society of America Journal 59,

26 Waine T.W., Blackmore B.S., Godwin R.J. (2000).Mapping available water content and estimating soil textural class using electro-magnetic induction. EurAgEng Paper 00-SW-044. AgEng 2000,Warwick,UK Webster R. and Oliver M.A. (2001) Geostatistics for environmental scientists, 2 nd Ed. John Willey & Sons, Ltd. Weinman B., Goodbred S.L., Zheng Y., Aziz A., Singhvi A.K., Nagar Y.C., Steckler M., van Geen A., Arsenic concentrations in shallow groundwater of Araihazar, Bangladesh: Part I. Geological control through floodplain evolution, submitted to Water Resources Research, January Williams B.G., and Hoey D. (1987) The use of electromagnetic induction to detect the spatial variability of the salt and clay contents of soil. Australian Journal of Soil Research 25, Yu W.H., Harvey C.M., Harvey C.F. (2003) Arsenic in groundwater in Bangladesh: A geostatistical and epidemiological framework for evaluating health effects and potential remedies, Water Resources Research 39 (6): Art. No Zheng Y., van Geen A., Stute M., Dhar R., Mo Z., Cheng Z., Horneman A., Gavrieli I., Simpson H.J., Versteeg R., Steckler M., Grazioli-Venier A., Goodbred S., Shanewaz M., Shamsudduha M., Hoque M.A., Ahmed K.M (2005) Geochemical and hydrogeological contrasts between 26

27 shallow and deeper aquifers in two villages of Araihazar, Bangladesh: Implications for deeper aquifers as drinking water sources, Geochimica et Cosmochimica Acta, 69(22),

28 Figure Captions Figure 1 Map showing the distribution of As in 840 wells <22 m (75 ft) deep surveyed by BGS/DPHE (2001). Surrounding topography based on the GTOPO30 digital elevation model of the USGS EROS Data Center. White rectangle shows the location of the Araihazar study area. Figure 2 (a) Map showing As concentrations in shallow wells <22 m (75 ft) deep tested for As (color dots) and 18,530 EM conductivity measurements (white lines of triangles) over a gray scale IKONOS satellite image of the Araihazar study area. The location of soil profiles collected with a hand-auger is shown by yellow crosses. (b) Map of EM conductivity in the fields of Araihazar indicated by color-coded triangles over a gray-scale IKONOS image. The locations of shallow wells are shown by white dots. Figure 3 Depth distribution of As in 5200 wells of Araihazar <22 m (75ft) deep. Also shown is a profile of As as a function of depth only (black line) and a range of profiles of As as a function of both EM conductivity and depth inferred from the linear regression of pairs of data up to 50 m apart (see Table 1). The 3 lines (blue, orange and green respectively) show the increase of As with depth calculated from the regression for EM conductivities of 9, 18 and 27 ms/m respectively. Figure 4 Histogram of EM conductivities measured in the Araihazar study area. 28

29 Figure 5 Profiles of conductivity measured in soil slurries collected at 14 locations in the Araihazar study area. The profiles are divided into three groups on the basis of the EM conductivity measured at the surface (a) 3-10 ms/m, (b) ms/m, and (c) ms/m. Figure 6 Profiles of the clay fraction of soils collected at 14 locations in the Araihazar study area. The profiles are grouped on the basis of the EM conductivity measured at the surface (a) 3-10 ms/m, (b) ms/m, and (c) ms/m. Figure 7 Regression of (a) slurry conductivity and (b) clay fraction integrated to a depth of 200 cm as a function of EM conductivity for 14 soil profiles collected in the Araihazar study area. The equation used to integrate the results is discussed in the text. Figure 8 Variogram for EM conductivity at an active lag of 1000 m and an active step of 10 m was constructed using GS+ v5.0 (Gamma Design, 2000). Small open circles show the experimental variogram; the solid line indicates the theoretical variogram. Figure 9 Cross validation curve which compares estimated and actual EM conductivities was constructed by removing each data point from the set in turn and then estimating its value by kriging (Robertson, 2000). This comparison (with r 2 of 0.90) shows that kriging adequately captures the main features of the variability in soil composition, with a standard error of predictions EM of ~2 ms/m 29

30 Figure 10 (a) Contour map of EM conductivity in the Araihazar study area derived from the variogram model discussed in the text. Contours are only shown for areas within 200 m of an actual EM measurement. (b) Comparison of the distribution of As concentrations in shallow wells of Araihazar with the contour map of EM conductivity. Arrows are pointing out the recharge rates at the four sites of multilevel wells calculated from 3 H- 3 He method (Stute et al., in revision). Figure 11 Box plot showing the relationship between EM conductivity and As concentrations in shallow wells for pairs of data up to 50 m apart. Figure 12 Comparison of measured As concentration with estimate calculated from regression as a function of both EM conductivity and depth for pairs of measurements within 50 m of each other. 30

31 Araihazar Arsenic concentration (ug/l) 0 to to to to to 2000 Figure1

32 90 35'15" 90 36'00" 90 36'45" 90 37'30" 90 38'15" 90 39'00" ð 3 ð ð11 Auger Locations 6 ð 12 ð7 8 ð ð ð 20 ð '15" 90 36'00" 90 35'15" 90 36'45" 90 36'00" 90 36'45" 90 37'30" '15" 90 37'30" 1 km 23 45'45" 23 45'45" ð7.5 ð '30" 23 46'30" '15" 23 47'15" As Concentrations (ug/l) 23 48'00" 23 48'00" 90 39'00" 90 38'15" 90 39'00" ð ð ð11 Auger Locations 6 ð 12 ð7 8 ð 12.5 ð ð '15" 90 36'00" 90 36'45" 90 37'30" Figure 2a & 2b 90 38'15" km 90 39'00" 23 45'45" 23 45'45" ð7.5 ð '30" 23 46'30" '15" 23 47'15" EM Conductivity (ms/m) 23 48'00" 23 48'00"

33 Groundwater As (ug/l) Depth Only 18 ms/m 9 ms/m 27 ms/m Depth (ft) Figure 3

34 Frequency More EM Conductivity (ms/m) Figure 4

35 Slurry Conductivity (ms/m) Slurry Conductivity (ms/m) Slurry Conductivity (ms/m) _1(7.5) 50 16_1(12.5) 50 15_5(35) 16_2(10) 15_4(15) 17_1(25) 16_3(8) 17_5(12) 17_2(22) Depth (cm) _4(6) 16_5(7) 18_2(3) Depth (cm) _1(11) Depth (cm) _3(20) Figure 5a, 5b & 5c

36 Clay (%) Clay (%) Clay (%) Depth (cm) _1(7.5) 16_2(10) 16_3(8) 16_4(6) 16_5(7) Depth (cm) _1(12.5) 15_4(15) 17_5(12) 18_1(11) Depth (cm) _5(35) 17_1(25) 17_2(22) 17_3(20) 18_2(3) Figure 6a, 6b & 6c

37 y = 3.03x(±0.5) (±2.57) r2 = 0.75 (n =14) EM31 (ms/m) Depth Integrated Slurry EC (ms/m) y = 1.49x (±0.19) (±1.44) r2 = 0.83 (n=14) EM31 (ms/m) Depth Integrated Clay (%) Figure 7a & 7b

38 Isotropic Variogram 1000m 75 Semivariance (ms/m) Distance (m) Exponential model (Co = 0.100; Co + C = ; Ao = 97.00; r2 = 0.825; RSS = 2266.) Figure 8

39 60 50 y = 1.008x± ±0.048 r 2 = Actual EM Cond :1 Line Predicted EM Cond Figure 9

40 EM Conductivity (ms/m) 0 to to to to to EM Conductivity (ms/m) As Concentrations (ug/l) 0 to to to to to m/y m/y A C B F 0.52 m/y Nests of multilevel wells Figure 10a & 10b 0.05 m/y EM Conductivity (ms/m)

41 Figure EM Conductivity (ms/m) As (ug/l)

42 Actual As Predicted As Figure 12

43 Table 1a: Outcome of univariate regression analysis of As concentrations as a function of EM conductivity (As = a*em Conductivity + C) for pairs of data separated by various distances. Distance a C r 2 P n 50m 7.2±0.8-19± m 8.9±0.8-39± m 7.9±0.7-24± m 7.2± ± >400m 2.6±0.5 35± Table 1b: Outcome of univariate regression analysis of As concentration as a function of depth only (As = a*depth + C) for pairs of data separated by various distances Distance A C R 2 P n 50m 4.0±0.3-89± m 3.7±0.3-77± m 3.7±0.3-65± m 3.0±0.3-18± >400m 2.2±0.2-27± Table 1c: Outcome of bivariate regression analysis of As concentrations as a function of both EM conductivity and depth (As = a*em Conductivity + b*depth + C) for pairs of data separated by various distances.. Distance a b C r 2 P n 50m 5.8± ± ± m 6.7± ± ± m 6.1± ± ± m 5.3± ±0.3-88± >400m 1.3± ±0.2-42±