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. Author manuscript; available in PMC: 2019 Oct 1.
Published in final edited form as: Water Resour Res. 2018 Aug 3;54(10):8160–8173. doi: 10.1029/2017WR022485

The Impact of Aquifer Flushing on Groundwater Arsenic Across a 35-km Transect Perpendicular to the Upper Brahmaputra River in Assam, India.

R Choudhury 1,^, B Nath 2, M R Khan 3, C Mahanta 1, T Ellis 2, A van Geen 2
PMCID: PMC6424526  NIHMSID: NIHMS984005  PMID: 30906078

Abstract

Well testing in the floodplain of the Brahmaputra River in Golaghat and Jorhat districts of Assam, India, shows that groundwater arsenic (As) concentrations increase with distance from the river. To establish the origin of this pattern, an additional 900 wells <60 m deep were tested for As and 9 sites were drilled along a 35-km transect perpendicular to the river. The field data show no relation between groundwater As concentrations ranging from <1 to 660 μg/L along the transect and (a) As concentrations of <1–5 mg/kg in cuttings of aquifer sand recovered while drilling or (b) the degree of reduction of iron oxides in these cuttings. The drilling indicates, however, a marked increase in the thickness of a clay layer capping the aquifer starting from <1–5 m near the river to over 60 m at the most distant site towards the base of the Naga foothills. Organic radiocarbon ages of 18–46 kyr obtained from all but one of 13 clay samples indicate pre-Holocene deposition of the underlying sands across the entire transect. Radiocarbon ages of dissolved inorganic carbon of 0.2, 4.7, and 17.8 kyr were measured in groundwater from 3 monitoring wells installed to 30–60 m depth at distances of 10, 20, and 40 km from the river, respectively. A conceptual groundwater flow model consistent with monitored heads and groundwater ages suggests that thick clay layers capping the aquifer further from the river inhibited flushing of the aquifer and, as a result, preserved higher As levels in groundwater.

Plain Language Summary

A large fraction of the rural population of South Asia living in river floodplain areas is exposed to toxic levels of arsenic by drinking well water. This study combines field data from the Indian state of Assam with a groundwater flow model to understand the mechanisms that control the distribution of arsenic in groundwater. A field kit was used to document groundwater arsenic concentrations that increase from the banks of the Brahmaputra River to the foothills of nearby mountains. Drilling showed that the sandy aquifer becomes isolated from replenishment with local surface water along the same transect by an increasingly thick impermeable capping clay layer. A simple groundwater flow model of the area tuned to match available data indicates that aquifers near the Brahmaputra River were flushed of much of their initial arsenic content by recharge with low-arsenic surface water over thousands of years. With increasing distance from the river, this flushing was progressively inhibited by a layer of clay layer capping the aquifer and elevated arsenic remained high as a result. Mitigation efforts to reduce arsenic exposure should take into account that the distribution of arsenic in well water evolves slowly and is therefore unlikely to change very soon.

1. Introduction

Elevated arsenic (As) concentrations in well water is a major public health concern for over 100 million people across South and Southeast Asia (Smedley and Kinniburgh, 2002; Ravenscroft et al., 2009). Reductive dissolution of Fe(III) oxyhydroxides minerals has emerged as the leading mechanism for the release of As to groundwater (Bhattacharya et al., 1997; Nickson et al., 1998; Berg et al., 2001; McArthur et al., 2004; Fendorf et al., 2010; Postma et al., 2012). Yet, several key factors including the relative importance of sedimentary and advected sources of reduced carbon for the dissolution of Fe(III) oxides remain poorly understood (Meharg et al., 2008; Neumann et al., 2010; Datta et al., 2011). Some studies provide evidence of a very shallow source of reactive carbon and/or reactive Fe oxides, both of which are required to release sequestered As from aquifer sediments to groundwater (Polizzotto et al., 2008; Neumann et al., 2010; Stuckey et al., 2016). Other observations suggest that advected organic matter and exchange of As with the aquifer sediment continue to play an important role further along the groundwater flow path (Postma et al., 2012; Mailloux et al., 2013; Radloff et al., 2017). This is probably one of the reasons why the documented heterogeneity of As concentrations in groundwater across a wide range of spatial scales remains poorly understood (BGS/DPHE, 2001; Ahmed et al., 2004; McArthur et al., 2008; Winkel et al., 2011).

In various attempts to understand the origin of heterogeneity, some have linked groundwater As occurrences to paleogeomorphology, near surface geology, depositional history and hydrogeology (Nath et al., 2005; van Geen et al., 2008; Hoque et al., 2009; McArthur et al., 2011; Donselaar et al., 2017). Weinman et al. (2008) pointed out that floodplain evolution over the past few thousand years seemed to control groundwater As heterogeneity in shallow aquifers of a study area in Bangladesh. Aziz et al. (2008) proposed for the same area that the spatially variable permeability of near-surface sediments contributed to aquifer As heterogeneity by influencing recharge rates of the underlying aquifer. Elsewhere in the Bengal basin, Nath et al. (2010) also showed that thick clay capping high-As aquifers inhibited direct recharge. Such observations led van Geen et al. (2008) to propose that recharge and transport over time reduce the concentration of exchangeable As in Holocene (<12 kyr old) sediment, and therefore also the concentration of As in groundwater in equilibrium with the solid phase.

Invoking processes acting over longer time scales, BGS/DPHE (2001) and McArthur et al. (2008, 2011) attributed lateral heterogeneity to elevated groundwater As levels in Holocene aquifers deposited within paleo-channels formed by erosion during sea-level low-stands. In contrast, low-As concentrations are characteristic of Pleistocene (>12 kyr) aquifers preserved within paleo-interfluves. Low-As concentrations in these Pleistocene aquifers have been attributed to re-oxidation of Fe oxides and/or flushing of an exchangeable As fraction during low sea-level stands (BGS/DPHE, 2001; Zheng et al., 2005).

Elevated levels of As in groundwater of the upper Brahmaputra floodplains were first reported more recently than elsewhere in South Asia (Singh et al., 2004; Borah et al., 2009; Chetia et al., 2010; Choudhury et al., 2015; Mahanta et al., 2015). Singh (2004) first suggested that abundant clay layers with a high organic carbon content in some parts of the state might have led to the release of As to groundwater. In the affected district of Jorhat (Fig. 1), on the southern bank of the Brahmaputra River, Chakraborti et al. (2004) and Goswami et al. (2013) reported dissolved As concentrations of almost 500 μg/L. Verma et al. (2016) pointed out the greater abundance of clay at a drill site in Jorhat compared to two drill sites on the northern bank of the Brahmaputra, but tentatively attributed higher As concentration in Jorhat to differences in tectonic history and sediment provenance. A joint study by UNICEF and the Assam Public Health Engineering Department (APHED) conducted with field kits documented As concentration above the World Health Organization guideline of 10 μg/L in 76 administrative blocks (Mahanta et al., 2015). Out of a total of 56,180 tested, 29% of wells contained As >10 μg/L and 8 % wells contained As >50 μg/L (Fig. 1a). In 2012, the national Bureau of Indian Standards (BIS) adopted a two-standard policy for As in drinking water: 10 μg/L as a recommended acceptable limit and 50 μg/L as a permissible limit in the absence of alternative sources that contain less As (BIS, 2012). The Assam PHED refers to these standards in its efforts to provide safe drinking water to rural communities (APHED, 2017).

Figure 1.

Figure 1.

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(a) Map showing As concentrations in shallow wells (<60 m depth) in the upper Brahmaputra River basin in Assam (Mahanta et al., 2015) along with the As distribution in the lower Brahmaputra River basin in Bangladesh (BGS/DPHE, 2001). The study area is marked by a rectangle where elevated As concentrations were observed to increase with distance from the river. (b) Google Earth image of the study area where elevated As levels (n=913) measured with a field kit are clustered near the base of the Naga Patkai Hill Ranges. Location of nine drilled boreholes (marked as triangles) along a 35-km transect (dashed line) is also shown. Symbol color corresponds to As concentration ≤10 (blue), 10–50 (green), and >50 μg/L (red), respectively.

The main motivation for selecting the Jorhat area south of the Brahmaputra River for further study was that As concentrations increase with distance from the river instead of decreasing with distance from the river, as documented further downstream in the Bengal Basin (BGS/DPHE, 2001; van Geen et al. 2008). In this paper, we first document in more detail the regional distribution of As in aquifers <60 m deep in a region straddling the border between Golaghat and Jorhat district with over 900 well tests using a field kit (Fig. 1b), a subset of which were verified by laboratory analysis. We then present lithological and geochemical data collected while manually drilling 9 sites along the 35-km transect that extends from the banks of the Brahmaputra River to the foothills of the Naga Patkai Hill ranges. The field data are complemented by radiocarbon dating of organic carbon in a set of clay cuttings recovered along the transect and of dissolved inorganic carbon in groundwater samples collected from monitoring wells installed along the same transect. The various observations are then linked with a simplified 2-D cross sectional groundwater flow model that extends from the center of the Brahmaputra river to the foothills of the Naga Patkai Hill ranges. The model shows that groundwater As concentrations increasing with distance from the river can be explained by differences in surface permeability and recharge rates. This leads us to propose that the new observations from Assam, as well as the opposite pattern of As concentrations declining with distance from the river documented further downstream in Bangladesh are actually controlled by the same set of processes.

2. Study Site and Methods

2.1. Study site

The study area is an alluvial plain in the upper Brahmaputra Valley bounded by the Brahmaputra River to the north, the Mikir Hills to the west, and the Naga Patkai Hill ranges to the south (Fig. 1). The main geomorphic units within this area are the floodplain of the Brahmaputra River in the north, a central upland area covering younger and older alluvial plains, and the piedmont plains to the south (CGWB, 2008). In addition to the Brahmaputra River, the fluvial sediments are supplied by two southern tributaries, the Dhansiri and Bhogdoi rivers, that drain the Naga Patkai Hill ranges and Mikir Hills of Quaternary age (Evans, 1932; Mathur and Evans, 1964). Shifts in the course of these rivers have been attributed to neotectonism (Sharma, 2005). Reports from previous drilling in the area to a depth of 400 m indicate a thick, multi-layered aquifer system comprised of medium to coarse grained sand (CGWB, 2008). Available lithologs indicate that the aquifer is capped to the east by a 30–50 m thick layer of clay and to the west by clay layers of more variable thickness.

2.2. Arsenic measurements with field kit

The Arsenic Econo-Quick test kit (Part No. 481298, Industrial Test Systems Inc., Rock Hill, SC, USA) was used to test a total of 777 private and public wells <60 m deep within a ~2,000 km2 area between June and August 2015, and an additional 136 wells closer to the drilling transect in November 2015. Groundwater samples were collected after hand-pumping for 3–5 min to purge each well. The kit relies on the 19th-century Gutzeit method, which is based on the generation of arsine gas from an acidified sample to which zinc powder is added. The arsine is trapped on and changes the color of a suspended test strip impregnated with mercuric bromide. The entire procedure requires less than 15 min. The color of the test strip is compared by eye with a reference chart corresponding to 9 bins of As concentrations in the 0–1,000 μg/L range. The oxidizing reagent (“Reagent 2”) was not used because no hydrogen sulfide levels in the well water sufficient to cause signal suppression was detected by smell. George et al. (2012) showed that kit measurements were consistent with laboratory measurements for well-water from Bangladesh, provided a systematic two-fold over-estimate of As concentrations above 50 μg/L by the kit is taken into account. Well depths recorded while testing are based on the household’s recollection, which is typically correct because it has witnessed every section of PVC it paid for go into the ground at the time of installation. The same kit was used to determine the effectiveness for As of a subset of 22 of the home-made iron removal filters that are used by most households in the area. These devices consist of a plastic or metal container partially filled with sand and a cloth filter and a hole at the bottom (Fig. S1).

2.3. Sediment and groundwater sampling

Sediment cuttings were collected from nine boreholes drilled along a 35-km transect perpendicular to the Brahmaputra river (Fig. 1b). Local drillers used the indigenous manual “hand-flapper” or “sludger” method for installing wells (Horneman et al., 2004). Cuttings were collected at 5-ft (1.5 m) intervals to a depth of up to 60 m and, in the case of sandy intervals, washed of their clay content using clean local well water to minimize the contribution from recycled drilling water. A pressed pellet of moist sediment from each interval was wrapped in transparent cling-wrap for archiving. At three of the drill sites located 5, 19, and 41 km from the river, respectively, monitoring wells screened at 45, 32, and 57 m depth were installed.

The field kit was also used to estimate in the field the exchangeable fraction of As in aquifer sands. About 0.5 g of freshly collected and washed sand was added to the kit’s reaction vessel along with 50 mL of (low-As) bottled water. The slurry was processed as a water sample (again without Reagent 2) and a visual reading of the strip against the reference chart was obtained after the standard 10 min reaction time.

2.4. Sediment reflectance

A CM700d diffuse reflectance spectrophotometer (Minolta Corp., USA) was used to quantify changes in the color of the freshly collected clay and sand cuttings through the cling-wrap at the drill site. The instrument was calibrated each day with the white barium sulfate plate provided by the manufacturer. Readings corresponded to an illuminant source of D65 (daylight-containing ultraviolet component, color temperature 6504K), with the observer angle set to 10°, and excluding the specular component (direct reflection). Previous work has shown that the proportion of Fe(II)/Fe in the acid-leachable fraction of Fe oxides in aquifer sediments of the Bengal Basin is linearly related to the difference in reflectance between 530 and 520 nm (Horneman et al., 2004). The drift in reflectance difference at these wavelengths over the course of the day for the calibration plate remained ≤0.01%.

2.5. Bulk sediment composition

Both clay and sand cuttings were also analyzed at the drill site for bulk composition through the cling-wrap at the drill site with an InnovX Delta Premium hand-held X-ray fluorescence analyzer mounted upside down in a portable stand. The samples were analyzed in the instruments “Soil” mode, for a total duration of 180 s at the instrument’s three incident X-ray energies. The calibration of the instrument was checked against NIST reference material SRM 2711 (Montana soil) at the beginning and the end of each day. A total of 14 readings for this standard over the course of the field work averaged 111±6 mg/kg relative to a certified value of 105±8 mg/kg. No correction was made to adjust for the coarse grain-size of the sand cuttings.

2.6. Arsenic measurements in the laboratory

To verify the performance of the kit in the groundwater matrix that may be different in Assam compared to Bangladesh, groundwater was collected without filtration in 20 mL polyethylene scintillation vials (Wheaton no. 986706, with PolySeal-lined cap) from a subset of 288 wells. One week prior to analysis by high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) on a Thermo-Finnigan Element2 (Cheng et al., 2004), the samples were acidified to 1% Optima HCl. By comparing duplicate samples acidified in the field and in the laboratory, van Geen et al. (2007) have shown that this protocol ensures that any precipitated Fe oxyhydroxides redissolves. The advantage of the method is that it eliminates the need for transporting concentrated HCl and reduces the chances of contamination. Samples of input and treated water from each of the 22 home-made iron removal systems were also analyzed by ICP-MS. An in-house consistency standard of artificial groundwater containing 430 μg/L As and reference materials NIST1640a (8.08±0.07 μg/L As) and NIST1643e (58.98±0.7 μg/L As) were included with every run to document accuracy and precision of the method to within <5%.

2.7. Radiocarbon dating of sediment and groundwater

To constrain the age of the aquifer, a total of 13 clay samples from 8 of the drill sites were selected for dating (no clay was encountered while drilling the site closest to the Brahmaputra River). The radiocarbon content of organic carbon in the clay samples was measured following standard procedures at the National Ocean Sciences Accelerator Mass Spectrometry Facility (NOSAMS) in Woods Hole, Massachusetts.

Groundwater samples were collected from the 3 monitoring wells for radiocarbon dating of dissolved inorganic carbon (DIC) in 250 mL glass bottles with Polyseal caps and poisoned with 0.2 ml of saturated HgCl2. Ratios of 14C/12C and 13C/12C in DIC were also measured at NOSAMS following established procedures (Elder et al., 1998).

2.8. Groundwater level measurements

Groundwater levels in three monitoring wells along with nearby paired shallow wells were recorded manually once a month from March to September 2015 using a Solinst water-level meter. The three paired shallow wells were household tubewells selected as additional monitoring wells for a better representation of water level changes along the transect. The paired shallow wells (LAN2 at a distance of 500 m from monitoring well LAN1; BAT3 1500 m from BAT1, and MAD2 500 m from MAD1) were the nearest available that the owner would allow us to open to lower the water level meter. In April 2018, groundwater levels were measured from an additional 13 wells screened in the 40–270 m depth range in an attempt to constrain the deeper portion of the groundwater flow model (Table S1). This second group of wells were either active or abandoned wells installed to feed Public Water Water Supply Schemes (PWWS). For all active wells, pumping was stopped at least 3 hours before the water level was measured. Water level data were referenced to the extent possible to a common datum using Shuttle Radar Topography Mission (SRTM) elevation data for three 30 m x 30 m grid points closest to each of the monitoring wells. Ground elevation at each of the wells was estimated at 82 m (LAN, 5 km from the Brahmaputra River), 93 m (BAT, 19 km), and 98 m (MAD, 41 km) relative to sea-level. The absolute uncertainty of SRTM elevations has been estimated elsewhere at ±16 m (Miliaresis and Paraschou, 2005) but the relative uncertainty appears to be considerably lower along the transect (Fig. 2).

Figure 2.

Figure 2.

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(a) Map showing As concentrations in shallow wells (<60 m depth) along the 35-km transect (n=242). Location of drilled boreholes marked by triangles labeled as in Table 2. Symbol color in (a) and (b) shows As concentrations in groundwater (blue ≤10 μg/L, green 10–50 μg/L and red >50 μg/L). White squares shows the location of wells listed in Table S1. (b) Depth distribution of As in groundwater along the transect. Radiocarbon ages for DIC in groundwater from three monitoring wells marked as diamonds are also shown. (c) Lithology inferred from boreholes drilled along the transect shown with radiocarbon ages for organic carbon contained in clay samples marked as diamonds. Grey open circles in c) correspond to the same wells as in b). Grey full line in b) and c) represents land surface elevation of the transect line inferred from SRTM. Note: asl denotes above sea level.

2.9. Groundwater flow and advective transport modeling

The US Geological Survey flow code MODFLOW (Harbaugh, 2005) was used to compare various simplified 2-D cross sectional models of the drilling transect. The modeled section is 50.5 km long with an average thickness of 400 m, approximating the thickness of the alluvium in this area. The model has a no-flow boundary at the bottom corresponding to the underlying basement rock (CGWB, 2013a, 2013b). Two different sets of models assume a no-flow boundary and a constant-head boundary for the vertical edge of the model near the hills, respectively (Fig. S2). Most of the model top is a recharge boundary with a constant topographic slope of 3 m/km. The only exception is the model top covering the half-width of the river, from river bank to the middle of the river, which is a constant head boundary equal to the annual average river stage of 74 m at this location. Another no-flow vertical boundary is placed at the river end of the model in the middle of the Brahmaputra River reflecting symmetry of the flow system on either side of the river. The entire model section is divided into 83 rows and 202 columns, resulting in a total of 16,766 cells. Various versions of the model assume recharge rate along the transect, different vertical boundary condition assigned at the hillside boundary of the model, or both. In one version of the model, the transect was subdivided into three areas with different recharge rates that reflect variations in the thickness of clay overlying the aquifer. Additional model simulations consider plausible upward and downward hydraulic head gradients along the foothill boundary of the model and different pumping scenarios based on the available information. All simulations were run for steady-state conditions.

Groundwater ages were simulated by forward particle-tracking using the USGS particle transport code MODPATH (Pollock, 2012). For the model with a no-flow boundary at the hillside, nine particles of zero age were placed at each model cell along the model top boundary (a recharge boundary) and tracked forward. For the model with a constant head boundary, nine additional particles per cell with an initial age of 18 kyr were placed near the hills and tracked in forward direction. In this case, the assumption is that the water at this location is recharged at distant locations and therefore already old.

3. Results

3.1. Arsenic in groundwater

Field kit data for a total of 913 wells <60 m deep partially covering the districts of Golaghat and Jorhat indicate that 33% of the wells in the area met the WHO guideline of 10 μg/L for As in drinking water, another 21% contained >10 to 50 μg/L, and 46% of samples exceeded 50 μg/L (Fig. 1b). The vast majority of wells within 10 km of the main course of the Brahmaputra contain <10 μg/L for As whereas most wells within 10 km of the Naga foothills contain >50 μg/L. Reported well depths near the river and the Naga foothills ranged 5 to 40 m and 20 to 60 m, respectively. Correcting for a 20 m difference in elevation from the Brahmaputra River to the Naga foothills, the screens of the vast majority of wells tested within a distance of 5 km of the transect therefore span the 40–80 m range in elevation (Fig. 2a, b).

Comparison of field and laboratory data for a subset of 288 wells confirms that the kit results are by and large consistent with the ICP-MS measurements. Samples giving kit readings of 0–10 μg/L, which averaged 3±6 μg/L by ICP-MS, and >50 μg/L (120±90 μg/L) misclassified 1% and 9% of wells, respectively (Table 1). A larger proportion of 53% of wells in the intermediate category with readings of 25–50 μg/L were incorrectly classified, but not a single well with >50 μg/L was classified by the kit as containing ±10 μg/L and vice-versa. Relative to the WHO guideline of 10 μg/L, the kit misclassified a total of only 6 (2%) out of all 288 tested wells (Fig. S3). The ICP-MS data also confirm that the kit overestimates As concentrations about a factor of two at concentrations >50 μg/L in Assam, as previously reported for wells in the Bengal basin (Fig. S3).

Table 1.

Comparison of As concentrations measured in the field using Arsenic Quick™ field test kit (ITS, Rock Hill, SC, USA) and the laboratory using ICP-MS. The proportion of wells correctly classified by the Arsenic Quick™ field test kit indicated in parentheses is relative to the sum in each column. The table is subdivided in quadrants by lines corresponding to the WHO guideline for As in drinking water.

As Field data
0–10 μg/L 25–50 μg/L 100–1000 μg/L
Lab data ±10 μg/L 84 (99%) 5 0
>10–50 μg/L 1 27 (47%) 13
>50 μg/L 0 26 131 (91%)
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The input water from 18 of the 22 selected wells whose water was treated with a home-made device ranged from 51 to 280 μg/L As (Fig. S4). The removal efficiency ranged from 36 to 92% and the treated water of 12 of these 18 wells contained <50 μg/L As. However, the treated water from only 4 of the same 18 wells with >50 μg/L met the WHO guideline of 10 μg/L for As in drinking water.

3.2. Lithology and redox state of aquifer sands

The borehole lithology shows a marked increase in the thickness of a surface clay layer starting from none to <5 m at the 3 sites closest to the river to 60 m of almost continuous clay at the most distant site near the base of the Naga foothills (Fig. 2b). Near the river, fine to medium grain sands dominate, while only thinner lenses of sands were encountered in boreholes closer to the hills. No sand was recovered during drilling to 25 m depth at the third closest site to the Naga Hills. Most of the sand across the transect are grey, with the exception of the site closest to the river where brown sand was recovered between 20 and 45 m depth and the next closest site with brown sand extending from 5 to 45 m depth (Fig. 2b). The reflectance data indicate particularly reduced Fe oxides with a difference in reflectance <0.1% (Horneman et al., 2004) prevail in the middle portion of the transect. In contrast, less reduced grey sands occur above the layer of brown sand at the site closest to the river and as thinner sand layers at two sites closer to the Naga Hills (Fig. 3a). Most clay samples along the transect are also grey, with the exception a few orange-brown clays at the site closest to the river and near the Naga Hills (Fig. S5).

Figure 3.

Figure 3.

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Profiles of properties of sand cuttings recovered from 8 of the 9 drill sites. (a) Difference in reflectance at 530 and 520 nm as proxy for sand color and the degree of reduction of Fe oxides (left=grey/reduced, right=brown/oxidized), concentrations of (b) Sr, and (c) As. Numbers next to each drilling site code indicate distance from the Brahmaputra River in kilometers.

3.3. Composition of aquifer sands

Under the counting time selected for field analysis by XRF, the concentrations of As in as many as 121 of the total of 147 samples of sand cuttings that were analyzed was below the limit of detection of 1.4 to 1.8 mg/kg calculated by the manufacturer’s software. For display, these readings were converted to concentrations corresponding to half the limit of detection and error bars extending from zero to the limit of detection (Fig. 3b). Concentrations of As in the remaining samples range from 1.5 to 4.6 mg/kg, without any clear pattern distinguishing drill sites close to the Brahmaputra River and the Naga Hills, respectively. Concentrations of As in 23 clay samples were below the limit of detection of 1.3 to 2.4 mg/kg, while the remaining 92 samples range from 1.6 to 27.7 mg/kg As, and again do not show a clear pattern across drill sites (not shown).

Analyzing sandy cuttings with the field kit resulted in readings limited to 0 and 10 μg/L As for all 49 samples of sand cuttings from the 5 sites closest to the river (Table 2). The corresponding range in leachable As concentrations is 0–1 mg/kg without adjustment in the reference chart. Out of the 9 samples of sand cuttings analyzed from the 4 sites closest to the Naga Hills instead, 3 samples gave readings of 25 and 50 μg/L corresponding to 2.5–5 mg/kg leachable As.

Table 2.

Arsenic concentrations measured with the kit in slurries of sand cuttings at 9 drill sites.

Borehole ID Distance from river (km) As (μg/L)
0 10 25 50
No. of samples
LAN 5 8 5 0 0
EKO 12 16 0 0 0
BON 15 6 0 0 0
BAT 19 3 3 0 0
SUM 23 2 6 0 0
MOH 25 0 2 1 0
BHO 30 0 0 0 0
BOS 36 1 0 0 0
MAD 41 1 2 0 2
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With the exception of Sr, there was no marked difference in the bulk composition of aquifer sands measured for other elements (K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Zr, Ba, and Pb) that were measured by XRF. Concentrations of Sr in all but one sample of sand cuttings from the 6 sites closest to the Brahmaputra River ranged from 130 to 300 mg/kg (Fig. 3c). In contrast, the Sr content of all 10 sand samples from the two sites closest Naga Hills was consistently lower and ranged from 50 to 120 mg/kg.

3.4. Radiocarbon ages

With the exception of one very shallow sample at 0.3 m below ground level (bgl) the 2nd closest site to the river, the uncorrected radiocarbon ages of organic matter in all clay cuttings ranges from 18 to 46 kyr (Fig. 2c, Table S2). At 3 of the 4 sites dated at more than one intervals, the radiocarbon age of the deeper interval is lower than for the shallower interval. At BAT, the age reversal could be because of ages 41 and 46 kyr are close to the practical limit for radiocarbon dating but this is not the case at BOS and MAD. Every effort was made to minimize the likelihood of shallow sediment falling into the hole while drilling and only samples contained within large chunks of clays were selected for dating. The age reversals closest to the foothills may therefore indicate the occasional transport and deposition of older reworked material. Overall, the data unambiguously indicate pre-Holocene (>12 kyr) deposition along most of the transect. The uncorrected DIC ages for groundwater span a wide range from 0.2 kyr at the site closest to the river to 4.7 kyr near the middle of the transect and 17.8 kyr closest to the Naga Hills (Fig. 2b, Table S2).

3.5. Groundwater levels and head gradient

Groundwater levels range across the transect from 2 to 10 m below the local ground surface (Table 3). When referred to a constant datum, shallow groundwater elevations decrease from 95 m near the hills to 80 m at the river, corresponding to a lateral head gradient of 0.3×10−3 (Fig. 4). Whereas there is no detectable vertical groundwater head gradient between the shallow and deeper well monitored closest to the river (LAN at 5 km), the groundwater level was about 2 and 5 m lower in the deeper well at BAT (19 km) and MAD (41 km) than shallower wells, respectively. Groundwater levels shoaled by about 1–2 m from March-May to July-September in three of the monitored wells but remained essentially constant in the other wells.

Table 3.

Variations in water level data relative to the local ground surface for 3 installed monitoring wells and 3 shallower existing wells along the transfect. Also listed are well depths relative to the local ground surface and estimated ground elevation based on SRTM.

Well ID Coordinates Depth (m) Elev. (m) Water level (m)
Feb Mar Apr May Jun Sep
LAN1 inst. well 26.7345 °N; 93.8947 °E 45 82 3.25 3.5 3.55 3.42 1.37 1.7
LAN2 exist. well 26.7350 °N; 93.8953 °E 8 82 3.2 3.35 3.37 3.35 1.32 1.76
BAT1 inst. well 26.6510 °N; 94.0114 °E 32 93 7.8 7.6 7.62 7.6 7.47 7.58
BAT3 exist. well 26.6525 °N; 94.0123 °E 8 93 6.1 6.3 6.35 6.3 5.87 5.45
MAD1 inst. well 26.5212 °N; 94.1709 °E 57 98 8 9.7 9.75 9.7 7.2 7.87
MAD2 exist. well 26.5230 °N; 94.1694 °E 26 98 2.4 2.5 2.5 2.5 2.4 2.92
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Figure 4.

Figure 4.

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Temporal variability of groundwater elevation six monitoring wells along the transect.

Groundwater level measured in mostly deeper wells along the transect in 2018 are not contemporaneous with the shallow head measurements but can still be used to place some limits on possible vertical head gradients. Taking into account the considerable uncertainty in surface elevation, groundwater levels in the 50–80 m and 120–270 elevation range were within a few meters of each other (Fig. S6) near the Naga Hills in 2018. Similarly, groundwater levels within the 10–80 m depth range measured in the 2015 and 2018 do not seem systematically different across the transect up to the Brahmaputra River.

3.6. Groundwater flow modeling

The groundwater modeling starts from the simplest case and then adds the minimum number of additional features needed to match the available observations. The case of the no-flow boundary near the hills implies that water in the aquifer originates solely from recharge across the top boundary. Constraints imposed by DIC radiocarbon ages are limited to the 35–50 m range in screen elevation but groundwater head data are available for screens installed down to −174 m in elevation. Recharge in the study has been estimated by CGWB (2013a, b) from seasonal water level changes and rainfall to be on the order of 250–300 mm/yr. For comparison, local precipitation averages 2000 mm/yr. Pumping tests in the same area indicate hydraulic conductivities ranging from 40 to 90 m/d (Table S4). The lower end of the conductivity range, however, produces hydraulic heads that are much higher than observed assuming isotropy, particularly towards the foothills where the aquifer cannot accommodate an imposed recharge of 270 mm/yr (Fig. S7a). By increasing the isotropic hydraulic conductivity to 80 m/d, the model reproduces more closely the observed heads for the same recharge (Fig. S7b). A similar fit can be obtained by maintaining the hydraulic conductivity at 40 m/d but reducing recharge to 90 mm/yr (not shown). However, the simulated groundwater ages for all these cases are <5 kyr throughout the section and do not vary laterally (Fig. S7a-d).

Although simulated head gradients increases if the vertical hydraulic conductivity is reduced by a factor of 100–10,000 relative to the horizontal hydraulic conductivity, anisotropy does not markedly increase the groundwater ages (Figs. S7c-e). Anisotropy on the order of 1000 has previously been used to model the Ganges-Brahmaputra basin (Michael and Voss, 2009). In order to fit the observation more closely, and assuming Kh/Kv = 1000, the recharge rate in zones 2 and 1 was sharply reduced by a factor of 10 and 100 relative to zone 3, respectively (Fig. 5a). The model prediction is not particularly sensitive to the choice of anisotropy (Fig. S7f-g). Groundwater ages still do not vary much laterally and, significantly, the distribution and range of simulated hydraulic gradients along the transect become inconsistent with the observations.

Figure 5.

Figure 5.

Open in a new tab

Simulated head and groundwater age distribution for a) Case-1b-4: no-flow boundary at the hills (0 km); Kh=80 m/d and Kv=0.08 m/d; recharge zone 1= 2.7 mm/yr, zone 2 = 27 mm/yr, zone 3 = 270 mm/yr. b) Case-2b3: Constant head of 94 m at the hills (0 km) with 18 kyr initial age of water entering that boundary; Kh=80 m/d and Kv=0.08 m/d; recharge zone 1= 2.7 mm/yr, zone 2 = 27 mm/yr, zone 3 = 270 mm/yr. Purple lines show head and the grey lines show flowpaths. Groundwater ages are shown by background color fill. The location and depths of the monitoring wells are shown as vertical white lines.

The model fit to both groundwater heads and ages improves considerably when using, instead, a constant head boundary near the hills (Fig. 5b). The assumption is that water recharges far from the transect and flows a considerable distance before reaching it. The groundwater is therefore already 18 kyr old when it reaches the hillside boundary of the transect and ages further while flowing towards the river. At the same time, younger groundwater recharged from the top flows towards the river on top of the older groundwater. Because there is little or no clay cap near the river, recharge from the top pushes the old groundwater downward by creating a shallow flow-system of young age water (<0.5 kyr); this is the base case scenario for subsequent model simulations. The basic pattern model prediction is again not very sensitive to the choice of anisotropy (Fig. S7h-i), however, it is highly sensitive the choice of spatial distribution of recharge (Fig. S7o). Also, the distribution of groundwater ages and heads remain consistent with the observation if the lowest value of K’s (Kh = 40 m/d, Kv = 0.04 m/d) obtained from pumptest data is used (Fig. S7p). The results are also not markedly different from the base case when either a downward decreasing or increasing head gradient of 4 m is introduced at the model boundary close to the Naga Hills (Fig. S7j-k).

The base case responds to various plausible pumping scenarios but again not in a way that drastically alters the predicted distribution of heads and ages within the depth ranges where this information was collected. According to CWGB (2013), groundwater withdrawals in the region amount to approximately 60 mm/yr. When such pumping is drawn from the shallow aquifer, older water is drawn up but not in a way that drastically alters groundwater ages at the bottom of the monitoring wells (Fig. S7l). In contrast, younger water is drawn down when pumping is imposed at greater depth again without drastically altering groundwater ages in the monitoring wells (Fig. S7m). The impact on the distribution of groundwater ages is essentially undectable when pumping is imposed from depths above and below the screen of the monitoring wells (Fig. S7n).

4. Discussion

4.1. Is groundwater As regulated by bulk sediment geochemistry?

The new groundwater data are consistent with results for Golaghat and Jorhat districts of Assam previously reported by Mahanta et al. (2015) and Verma et al. (2016) but fill gaps in the coverage to produce a clear regional pattern of groundwater As concentrations steadily increasing from the banks of the Brahmaputra River to the Naga foothills (Fig. 1b). The increase in groundwater As concentrations with distance from the river observed in Assam can only partially be explained by difference in the extent of reduction of Fe oxides in aquifer sands. Groundwater As levels are very low near sites LAN and EKO closest to the river which contain intervals of oxidized brown sands (Fig. 2b). Even the reflectance profile at LAN also indicates a sizeable layer of grey sands. Groundwater As concentrations are still generally low northwest of site BON, the 3rd-closest to the river, even though sands are uniformly grey at this location. The most telling indication that the redox state of aquifer sediments alone cannot account for variations in groundwater As are reflectance data showing particularly reduced sands prevailing in the middle portion of the transect instead of the region closest to the Naga Hills (Fig. 3a).

Pickering et al. (2013) and Goodbred et al. (2014) have shown that bulk Sr concentrations can be used to distinguish Brahmaputra River sediments in Bangladesh derived from Sr-rich (140 mg/kg) Tibet and Sr-depleted terranes of the Himalaya and Shillong Massif (<90 mk/kg). On this basis, a source other than Tibet can be assigned only to sands recovered from sites BOS and MAD closest to the Naga Hills (Fig. 3b).

Bulk As concentrations in sand cuttings recovered during drilling are not related in any systematic way to groundwater As concentrations along the transect (Fig. 3c). Whereas sites BOS and MAD cover the region where the groundwater As are most consistently elevated, many wells near the middle of the transect are also elevated in As. It therefore does not appear that either the redox state, provenance, or the bulk As content of aquifer sands can explain the main trend of groundwater As concentrations across the transect.

4.2. Is groundwater As regulated by hydrogeology?

In contrast to bulk As concentrations, concentrations of leachable As in the sediment estimated with the field kit do broadly follow that of groundwater As concentrations and increase from the Brahmaputra River towards the Naga Hills. Using a somewhat more involved method to displace As from adsorption sites, van Geen et al. (2008) showed that this type of relation between exchangeable As in Holocene sediment and groundwater As can be attributed to equilibrium exchange. Sizeable concentrations of exchangeable As in shallow grey sands has been confirmed by a series of push-pull experiments in Bangladesh during which low-As water was pumped into a high-As aquifer and vice-versa (Radloff et al., 2017). The broad increase in both groundwater As concentrations and exchangeable As levels in grey sediment from the river to the hills can therefore plausibly be attributed to some form of equilibration, even if the sediment is of Pleistocene rather than Holocene age.

The pool of exchangeable As in aquifer sands is much larger than in groundwater. Under the assumption of equilibrium exchange, the pattern of groundwater As concentrations in the study area therefore requires a mechanism that explains the increase in exchangeable As in the solid phase towards the Naga Hills. The groundwater flow model that most closely matches the available data indicates that flushing over time could be the primary cause of the observed increase in exchangeable As and groundwater As from the Brahmaputra River to the Naga Hills. Orange sands are excluded from the argument because their Fe oxide coatings are insufficiently reduced to maintain a pool of exchangeable As (van Geen et al., 2004; Zheng et al., 2005). Near the river, the model indicates groundwater ages of only a few hundred years down to several tens of meters because of the permeability of the surface soils and proximity to the river discharge area (Fig. 5b). The available As data suggest that the resulting flushing was sufficient to lower the amount of exchangeable As in these shallow Pleistocene sands. With increasing distance from the river, however, the age of groundwater, and therefore its residence time, increases much more rapidly with depth to several thousand years and higher (Fig. 5b). This much more sluggish circulation, consistent with the radiocarbon ages, apparently preserved a larger pool of exchangeable As, and therefore, maintain a higher As concentrations in groundwater towards the Naga Hills. Various versions of the model show that the separation between a shallow rapid and a deep slower groundwater circulation system is not particularly sensitive to the choice of parameters or perturbation by pumping (Fig. S7). Alternative explanations cannot be ruled out but this is the simplest that is consistent with all the data and process studies conducted elsewhere in South Asia.

4.3. Arsenic mitigation

Analysis of effluent from a limited number of home-made filters used to remove Fe from groundwater shows that As concentrations were brought down to <50 μg/L from higher levels in two-third of cases, but <10 μg/L for only one quarter of these. Unlike in Vietnam, where Fe-removal at the household level is particularly effective at removing As from groundwater because of relatively high Fe and low phosphate levels (Berg et al., 2006), additional measures providing access to low-As drinking water are clearly needed to lower the exposure of the rural population of Assam. Our survey included 4 wells >90 m deep (not shown) located near the study transect that met the WHO guideline value of 10 μg/L for As in drinking water. However, none of these are located within the area where most shallower wells are high in As.

In Bangladesh, the government’s Department of Public Health Engineering has installed several hundred deep wells throughout the country intended as public sources of low-arsenic drinking water (Ravenscroft et al. 2014), but the Assam Public Health Engineering Department has not conducted the drilling necessary to explore this approach. Instead, the APHED has installed over the past 40 years several thousand piped-water supply schemes to meet drinking water needs of the population, including 994 schemes in Golaghat and Jorhat districts that are connected to 3,548 delivery points (IMIS, 2017). The source water for these systems is typically groundwater processed through a Fe-based As removal plant where needed. The water is also chlorinated before delivery and tested for As by APHED district laboratories. Our testing of 6 such systems indicated As concentrations >50 μg/L in one case, two with 10–50 μg/L, and three containing <10 μg/L As. An alternative approach to treatment, at least in areas with mixed distribution of low- and high-As wells, might be to deliver through the existing pipes groundwater from existing wells that are already low in As. The time scale of the flushing that reduced exchangeable As levels in shallow aquifers from presumed high initial levels suggests that considerable volumes could be pumped from the low-As wells before drawing in As from adjacent contaminated aquifer.

The total population of Golaghat and Jorhat districts is 2 million, with an estimated 590,000 people living within the four blocks (Golaghat East and Gomariguri in Golaghat districts and Jorhat and Titabor in Jorhat districts) where the vast majority of tested wells (358 out of 481 wells tested) exceed 50 μg/L As, the permissible limit in the absence of alternative drinking water source in India (BIS, 2012). According to the Indian government’s Ministry of the Drinking Water and Sanitation (IMIS, 2017), these piped-water supply schemes provide full access to low-As water to 278,000 inhabitants within the four heavily As contaminated blocks, and partial access to another 184,000 inhabitants, none to the remaining 128,000 inhabitants. Those villagers most in need will hopefully be targeted by additional water treatment systems in the planning with support from the Government of India and the World Bank.

5. Conclusions

The increase in shallow groundwater As concentrations with distance from the Brahmaputra River documented in this study is the opposite of the pattern observed downstream in Bangladesh. The underlying mechanism linking recharge and As in shallow aquifers, however, appears to be similar in the two regions. The combined set of observations indicate that surface permeability regulates the rate of recharge and flushing over centuries to thousands of years and, therefore, the level of As in shallow groundwater. Low-As aquifers could therefore probably be used more extensively as a source to reduce the exposure of villagers in the more affected portions of the region, at least in the short- to medium-term.

Supplementary Material

Supp info

Key Points:

  • Arsenic levels in shallow wells increase over the 35 km separating the banks of the Brahmaputra River from the Naga foothills in Assam.

  • The thickness of clay capping the >12 kyr-old aquifer also increases <1 to 60 m from the banks of the river to the foothills.

  • A model consistent with groundwater heads and ages suggests arsenic was flushed out of the aquifer more effectively near the river.

Acknowledgments

This study was supported by a grant from CE/C/CM/57 to CM, a Fulbright fellowship to RC, US National Institute of Environmental Health Sciences grant P42 ES10349, and US National Science Foundation grant NSF ICER 1414131. Key data collected under this project are provided as tables in the text and Supplemental Information. Additional data is archived at http://www.hydroshare.org/resource/bbe23dfacab647568a18dc338114d6d7. We are immensely grateful to Ubadur Rahman Neog, Abidur Rahim, and Dilip Kakoti from the Assam Public Health Engineering Department for their generous support and collaborative spirit provided throughout the project. We also thank students from IIT Guwahati, Dr. Chander Kumar Singh from TERI Institute for Advanced Studies, and Mr. Imtiaz Choudhury from the University of Dhaka for their help in the field. This is Lamont-Doherty Earth Observatory contribution no. 8238.

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