The potential of major ion chemistry to assess groundwater vulnerability of a regional aquifer in southern Quebec (Canada)
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Abstract
Groundwater vulnerability mapping provides useful but limited information for developing protection plans of the resource. Classical vulnerability ranking methods often do not take into account complex hydrostratigraphy and never consider groundwater flow dynamics. The objective of this work was to test the potential of major ion chemistry to assess regional-scale intrinsic groundwater vulnerability. Because it reflects water–sediment and water–rock interactions, the new vulnerability index reflects both infiltration processes and groundwater hydrodynamics. The method was applied on a regional fractured bedrock aquifer located in the Becancour region of southern Quebec (Canada). In this region, hydrogeochemistry shows that freshly recharged groundwater evolves from (Ca, Mg)–HCO3 and Ca–SO4 to Na–HCO3 type with gradually increasing confinement conditions in the fractured aquifer and tends to Na–Cl type locally by mixing with trapped marine pore-water. The new method identified recharge areas as those of the highest vulnerability and gradually decreasing vulnerability as confinement of the aquifer increased. It also highlights local discontinuities in confinement that differ from the regional pattern. Results showed a good correlation between groundwater vulnerability estimated with the new method and nitrate occurrence in groundwater. Eighty-two per cent of all samples presenting detectable nitrate concentrations were characterized by a Hydrogeochemical Vulnerability Index greater than 9 (maximum is 10). The ability of the new vulnerability method to identify areas vulnerable to detectable nitrate concentrations was much higher than that deriving from the DRASTIC method. This work confirms that major ions chemistry contains significant information about groundwater vulnerability and could be used to improve groundwater resource management.
Keywords
Groundwater Vulnerability Hydrogeochemistry Fractured bedrock aquifer Quebec (Canada)Introduction
In the last decades, worldwide increases in groundwater contamination (UNEP 2003) have brought attention to the concept of groundwater vulnerability and led to the development of specific mapping techniques. Groundwater vulnerability is generally defined as the capacity of a hydrogeological system to transfer a contaminant from the land surface to the saturated zone. This concept is continuously evolving, and several definitions have been proposed. For example, intrinsic vulnerability (Vrba and Zoporozec 1994) and intrinsic sensitivity (EPA 1993) usually refer to groundwater vulnerability due to the hydrogeological context independently of land use or any information about the spatial distribution of potential sources of contamination. The specific vulnerability (Andrade and Stigter 2009) integrates contaminant-specific parameters, such as half-life or sorption coefficient to soil organic matter when assessing groundwater vulnerability to pesticides. In reference to the source-pathway-target concept, the contaminant pathway for “aquifer vulnerability” is the vertical path from the ground surface to the water table. When considering “well vulnerability” (Frind et al. 2006), the contaminant pathway is the flow path from an upgradient source at ground surface to a given well.
Many methods are available to estimate intrinsic groundwater vulnerability. Ranking or index-based aquifer vulnerability methods such as DRASTIC (Aller et al. 1987), AQUIPRO (Chowdhury et al. 2003) or LHT (Mansoor et al. 2014) might be the easiest methods to implement using available physical data. These methods are currently widely used in water management as land-use planning tools and often reported in the scientific literature (see Saidi et al. 2010; Tilahun and Merket 2010; Chen et al. 2013; Mansoor et al. 2014 for recent applications of the DRASTIC index). However, index methods raise criticism because they often fail to predict the occurrence of contaminants in groundwater (Mehnert et al. 2005; Stigter et al. 2008). One reason could be because indexed aquifer vulnerability methods do not take into account groundwater flow dynamics, which may control the lateral migration of contaminants from the land surface to the aquifer (Kozuskanich et al. 2014). The scientific literature holds many examples of modification and adaptation of index methods intended to increase their reliability in specific contexts or to increase their efficiency to correlate with contaminant occurrence (Bojórquez-Tapia et al. 2009; Zhou et al. 2012; Chen et al. 2013).
The estimation of travelling time through the vadose zone could serve as an indicator of aquifer vulnerability (Van Stempvoort et al. 1992), but requires data extracted from geological 3D models which are often unavailable. At the opposite end, numerical models of solute transport through vadose and saturated zones (Yu et al. 2014) aim to represent all relevant processes influencing well vulnerability. These methods integrate flow dynamics and are very useful for assessing the risk of anthropogenic pollution to potable water supplies (Frind et al. 2006), but have large data requirements (Nobre et al. 2007).
Another way to integrate groundwater flow processes is to consider groundwater geochemistry. Major ions water chemistry (Ca, Mg, K, Na, Cl, HCO3 and SO4) is the product of water–soil and water–rock interactions (e.g. Barbecot et al. 2000; Saxena and Ahmed 2001). Because they depend on flow paths and on groundwater residence times, major ion facies are often used to determine the natural baseline composition of groundwater (Edmunds et al. 2003) or to distinguish between contrasted hydrogeological contexts (Cloutier et al. 2008). Hydrogeochemistry can also be useful to assess groundwater quality through statistical regression of chemical trends (Stigter et al. 2008). Multivariate analyses of geochemical data have been used to build groundwater quality indices and typically include solutes from natural and/or anthropogenic origin (Saeedi et al. 2009).
Hydrogeochemistry closely reflects input from recharge areas, as well as confinement conditions and groundwater travel times. It is therefore a proxy for groundwater vulnerability. Recharge areas represent direct links between the surface and the saturated zone, characterized by a distinct water signature. Aquifer confinement usually implies a longer rock–water contact time. And, as travel time increases, the possibility of degradation (e.g. denitrification; see McMahon et al. 2008), cation adsorption (e.g. on clay matrix; Andrade and Stigter 2009) and dilution within the regional aquifer flow also increase. Although groundwater quality indices are useful to distinguish more or less vulnerable areas, they mostly portray regional quality of groundwater (Sorichetta et al. 2013). Mendizabal and Stuyfzand (2011) have explicitly defined an intrinsic well vulnerability index by integrating several hydrogeochemical parameters (i.e. pH, redox level, alkalinity and groundwater age as pre- or post-tritium bomb peak). However, this method does not focus on the basic evolution of the major ion composition of groundwater within various hydrogeological contexts.
Hydrogeochemistry has the potential to represent complex influential patterns regarding well vulnerability which otherwise could be extremely difficult to determine at the regional scale with physical approaches. The objective of this work was to test the potential of major ion chemistry to assess regional-scale intrinsic groundwater vulnerability. A new vulnerability index based on the groundwater major ions facies is applied to a regional-scale fractured bedrock aquifer located in the Becancour River watershed in southern Quebec (Canada). The vulnerability index is compared to a regional groundwater vulnerability map drawn with the DRASTIC index, and the capacity of both methods to correlate with measured groundwater nitrate concentrations is assessed.
Geological and hydrogeological setting
The regional aquifer is located in the fractured bedrock of the St. Lawrence Platform. This aquifer is mainly unconfined in the Appalachian Mountains and in the Appalachian Piedmont, progressively becoming semi-confined, north-westerly. In a 10-km-wide zone bordering the St. Lawrence River, the aquifer is confined by thick marine clays of the Champlain Sea that allow limited recharge (Larocque et al. 2013). The bedrock aquifer within the study area has relatively low hydraulic conductivity (~10−9–10−7 m/s). Wells in the fractured bedrock aquifers yield enough water to supply single-family dwellings and small municipalities (~500–5000 habitants). Higher-yield aquifers (~10−3–10−4 m2/s) are found in coarse-grained surficial sediments, such as the Quaternary glaciofluvial or fluvial sediments. These are often separated from the fractured bedrock aquifer by a basal till unit (Becancour till; Lamothe 1989), which acts as an aquitard. Regional groundwater flow is SE–NW and usually follows the topography, with recharge occurring mainly in the Appalachian Mountains and discharge in the main tributaries of the St. Lawrence River. Local recharge areas are also found in the St. Lawrence Lowlands, particularly in the absence of Champlain Sea clays, along a band located just north-west of the Appalachian Piedmont. Larocque et al. (2013) estimated the average regional recharge to be 159 mm/year.
Land use is mainly forestry (48 %) and agricultural (40 %), with the significant presence of wetlands (8 %) and few urban areas (4 %). Cereals and most intensive crops are found mainly along the St. Lawrence and Becancour rivers, while the Appalachian valleys are associated with pastures and dairy farms (see Larocque et al. 2013 for more information on land use). The average annual temperature is 4.5 °C, and the total precipitation is 1100 mm/year, with 25 % falling as snow between November and March (Environment Canada 2012).
Analytical methods and groundwater sampling
Hydrogeochemical data
Sample name | Total well depth (m) | Ca (mg/L) | Mg (mg/L) | K (mg/L) | Na (mg/L) | HCO3 (mg/L) | Cl (mg/L) | SO4 (mg/L) | N–NO3 (mg/L) |
---|---|---|---|---|---|---|---|---|---|
BEC001 | 51.8 | 5.9 | 0.9 | 0.4 | 130.0 | 316.8 | 13.0 | 63.0 | 0.0 |
BEC002 | 17.1 | 94.0 | 21.0 | 3.4 | 31.0 | 353.7 | 52.0 | 40.0 | 0.0 |
BEC003 | 22.9 | 72.0 | 6.8 | 3.6 | 16.0 | 219.5 | 37.0 | 21.0 | 0.0 |
BEC005 | 18.3 | 3.5 | 0.7 | 4.1 | 210.0 | 414.7 | 47.0 | 100.0 | 0.0 |
BEC007 | 44.0 | 8.4 | 3.1 | 2.0 | 87.0 | 241.7 | 19.0 | 15.0 | 0.0 |
BEC008 | 17.0 | 2.2 | 0.7 | 1.5 | 320.0 | 411.5 | 280.0 | 24.0 | 0.0 |
BEC009 | 50.3 | 32.0 | 3.1 | 2.9 | 9.9 | 146.3 | 1.8 | 0.0 | 0.0 |
BEC010 | 37.0 | 32.0 | 6.6 | 2.0 | 4.9 | 121.9 | 0.9 | 24.0 | 0.0 |
BEC011 | 22.0 | 13.0 | 1.7 | 2.0 | 55.0 | 182.7 | 0.7 | 23.0 | 0.0 |
BEC012 | 32.6 | 58.0 | 7.7 | 2.7 | 5.1 | 158.5 | 52.0 | 6.3 | 0.0 |
BEC014 | 41.1 | 2.2 | 0.7 | 1.3 | 250.0 | 389.9 | 190.0 | 3.1 | 0.0 |
BEC015 | 33.5 | 3.0 | 1.0 | 1.5 | 63.0 | 181.6 | 0.6 | 14.0 | 0.0 |
BEC016 | 26.0 | 15.0 | 2.6 | 2.7 | 61.0 | 182.7 | 32.0 | 0.0 | 0.0 |
BEC021 | 25.9 | 45.0 | 14.0 | 2.4 | 18.0 | 146.2 | 46.0 | 22.0 | 0.0 |
BEC022 | 45.7 | 3.0 | 0.5 | 1.4 | 110.0 | 230.6 | 48.0 | 0.5 | 0.0 |
BEC024 | 27.4 | 2.6 | 0.4 | 1.0 | 96.0 | 230.6 | 0.8 | 37.0 | 0.0 |
BEC026 | 10.7 | 130.0 | 21.0 | 5.7 | 51.0 | 451.3 | 79.0 | 28.0 | 0.0 |
BEC027 | 21.0 | 20.0 | 2.2 | 2.0 | 86.0 | 231.6 | 16.0 | 33.0 | 0.0 |
BEC028 | 30.5 | 26.0 | 2.6 | 3.3 | 95.0 | 170.7 | 85.0 | 41.0 | 0.0 |
BEC029 | 27.4 | 57.0 | 6.2 | 3.7 | 9.6 | 146.3 | 37.0 | 24.0 | 0.0 |
BEC030 | 27.7 | 2.3 | 0.1 | 2.9 | 85.0 | 182.6 | 0.7 | 46.0 | 0.0 |
BEC031 | 18.3 | 23.0 | 6.7 | 6.4 | 43.0 | 195.1 | 12.0 | 17.0 | 0.0 |
BEC032 | 21.0 | 130.0 | 9.9 | 3.9 | 8.8 | 402.5 | 14.0 | 26.0 | 1.0 |
BEC033 | 49.4 | 7.9 | 1.2 | 3.8 | 84.0 | 206.9 | 2.4 | 44.0 | 0.0 |
BEC034 | 24.4 | 27.0 | 3.9 | 1.9 | 16.0 | 121.9 | 2.7 | 18.0 | 0.0 |
BEC035 | 38.1 | 9.7 | 1.3 | 2.4 | 94.0 | 158.1 | 64.0 | 25.0 | 0.0 |
BEC036 | 58.5 | 100.0 | 34.0 | 1.6 | 30.0 | 353.7 | 75.0 | 48.0 | 0.5 |
BEC040 | 14.5 | 19.0 | 1.9 | 1.0 | 2.3 | 47.57 | 3.6 | 12.0 | 2.6 |
BEC041 | 61.0 | 55.0 | 5.4 | 0.4 | 1.4 | 158.5 | 2.3 | 24.0 | 0.9 |
BEC042 | 80.8 | 41.0 | 1.4 | 0.4 | 5.0 | 120.7 | 7.2 | 8.0 | 0.1 |
BEC043 | 44.2 | 2.5 | 1.3 | 2.3 | 310.0 | 426.7 | 310.0 | 3.4 | 0.0 |
BEC044 | 84.2 | 97.0 | 2.9 | 0.3 | 5.8 | 121.9 | 0.7 | 180.0 | 0.0 |
BEC045 | 70.1 | 69.0 | 3.9 | 0.9 | 5.4 | 207.3 | 5.7 | 27.0 | 0.4 |
BEC046 | 68.6 | 40.0 | 15.0 | 0.8 | 6.8 | 158.5 | 18.0 | 26.0 | 0.0 |
BEC050 | 38.1 | 40.0 | 11.0 | 3.8 | 11.0 | 158.5 | 6.4 | 42.0 | 0.0 |
BEC052 | 88.4 | 14.0 | 2.1 | 2.4 | 85.0 | 243.9 | 5.8 | 43.0 | 0.0 |
BEC053 | 39.0 | 88.0 | 17.0 | 3.3 | 18.0 | 341.5 | 18.0 | 48.0 | 0.0 |
BEC055 | 18.3 | 54.0 | 6.3 | 1.3 | 7.7 | 158.5 | 13.0 | 34.0 | 0.0 |
BEC056 | 100.6 | 24.0 | 9.1 | 3.2 | 180.0 | 353.7 | 120.0 | 51.0 | 0.5 |
BEC057 | 20.7 | 32.0 | 5.4 | 2.4 | 85.0 | 268.3 | 55.0 | 9.9 | 0.0 |
BEC100 | 51.8 | 8.9 | 1.0 | 0.8 | 38.0 | 105.0 | 7.6 | 20.0 | 0.7 |
BEC101 | 47.2 | 1.2 | 0.3 | 1.1 | 210.0 | 413.8 | 150.0 | 2.2 | 0.0 |
BEC102 | 21.6 | 55.0 | 8.3 | 2.4 | 44.0 | 268.3 | 37.0 | 12.0 | 0.0 |
BEC103 | 43.6 | 73.0 | 13.0 | 3.1 | 40.0 | 268.3 | 62.0 | 41.0 | 0.0 |
BEC104 | 28.0 | 24.0 | 2.3 | 1.0 | 6.3 | 76.82 | 11.0 | 8.6 | 0.4 |
BEC107 | 36.6 | 33.0 | 5.4 | 0.8 | 5.8 | 101.1 | 9.6 | 32.0 | 0.1 |
BEC110 | 37.8 | 43.0 | 11.0 | 1.8 | 29.0 | 195.1 | 5.8 | 60.0 | 0.0 |
BEC112 | 38.1 | 32.0 | 11.0 | 2.0 | 24.0 | 207.3 | 1.7 | 8.1 | 0.1 |
BEC115 | 68.6 | 24.0 | 7.2 | 0.3 | 0.8 | 98.81 | 0.4 | 7.6 | 0.1 |
BEC116 | 45.7 | 26.0 | 7.2 | 1.2 | 3.1 | 98.81 | 4.8 | 9.3 | 2.4 |
BEC119 | 45.7 | 21.0 | 6.9 | 2.6 | 53.0 | 170.7 | 43.0 | 0.0 | 0.0 |
BEC120 | 68.6 | 21.0 | 7.5 | 2.2 | 96.0 | 292.6 | 34.0 | 17.0 | 0.0 |
BEC121 | 99.1 | 3.3 | 0.9 | 1.8 | 270.0 | 512.2 | 150.0 | 0.0 | 0.0 |
BEC122 | 38.1 | 29.0 | 13.0 | 4.4 | 60.0 | 317.1 | 3.3 | 1.0 | 0.0 |
BEC123 | 54.9 | 45.0 | 3.1 | 1.1 | 5.3 | 115.8 | 5.3 | 32.0 | 0.3 |
BEC126 | 49.1 | 25.0 | 6.2 | 0.9 | 27.0 | 134.1 | 26.0 | 7.6 | 0.0 |
BEC127 | 25.9 | 30.0 | 1.4 | 0.4 | 3.4 | 79.29 | 5.4 | 11.0 | 1.9 |
BEC129 | 61.0 | 36.0 | 4.0 | 1.5 | 47.0 | 207.3 | 23.0 | 22.0 | 0.1 |
BEC130 | 91.4 | 150.0 | 22.0 | 2.4 | 16.0 | 426.9 | 34.0 | 93.0 | 0.0 |
BEC131 | 22.9 | 30.0 | 8.0 | 0.4 | 3.0 | 117.1 | 2.2 | 9.7 | 2.0 |
BEC132 | 38.1 | 39.0 | 10.0 | 1.4 | 2.1 | 134.1 | 3.0 | 20.0 | 3.8 |
BEC134 | 38.1 | 97.0 | 7.0 | 2.8 | 6.6 | 256.1 | 6.1 | 63.0 | 2.0 |
BEC135 | 44.2 | 54.0 | 6.1 | 1.0 | 3.8 | 170.7 | 1.9 | 18.0 | 1.9 |
BEC137 | 23.7 | 5.1 | 0.4 | 1.6 | 85.0 | 219.5 | 9.4 | 29.0 | 0.0 |
BEC138 | 32.0 | 42.0 | 3.3 | 1.4 | 6.0 | 158.5 | 2.7 | 2.4 | 0.0 |
BEC139 | 48.8 | 0.1 | 0.0 | 0.1 | 88.0 | 95.13 | 11.0 | 100.0 | 6.1 |
BEC140 | 33.5 | 28.0 | 1.6 | 1.6 | 1.0 | 100.0 | 0.2 | 5.0 | 0.2 |
BEC142 | 68.6 | 44.0 | 13.0 | 1.2 | 8.3 | 195.1 | 8.0 | 16.0 | 1.6 |
BEC143 | 51.2 | 30.0 | 2.1 | 0.4 | 4.5 | 108.5 | 0.5 | 19.0 | 0.0 |
BEC144 | 8.2 | 100.0 | 17.0 | 6.2 | 21.0 | 439.1 | 11.0 | 40.0 | 0.0 |
BEC145 | 13.7 | 35.0 | 18.0 | 8.0 | 71.0 | 304.9 | 53.0 | 22.0 | 0.0 |
BEC146 | 30.5 | 35.0 | 9.3 | 2.3 | 31.0 | 219.5 | 9.0 | 17.0 | 0.0 |
BEC149 | 54.9 | 50.0 | 4.0 | 2.0 | 15.0 | 195.1 | 5.7 | 14.0 | 0.1 |
BEC160 | 144.8 | 41.0 | 2.1 | 1.0 | 4.5 | 113.4 | 9.5 | 7.4 | 3.7 |
BEC161 | 8.5 | 33.0 | 3.5 | 1.6 | 15.0 | 146.3 | 4.1 | 14.0 | 0.0 |
BEC165 | 106.7 | 220.0 | 31.0 | 3.5 | 500.0 | 426.9 | 1100.0 | 74.0 | 0.9 |
BEC166 | 73.2 | 20.0 | 6.9 | 3.9 | 210.0 | 451.0 | 120.0 | 36.0 | 0.0 |
ROC01 | 30.0 | 13.0 | 2.0 | 0.6 | 3.4 | 31.71 | 2.2 | 9.5 | 2.5 |
ROC03 | 78.3 | 24.0 | 9.4 | 6.8 | 820.0 | 340.9 | 970.0 | 3.0 | 0.0 |
ROC04 | 36.6 | 6.8 | 3.3 | 0.8 | 85.0 | 206.5 | 2.4 | 33.0 | 0.0 |
ROC05 | 47.2 | 1.9 | 0.5 | 1.6 | 140.0 | 365.2 | 3.0 | 16.0 | 0.0 |
ROC06 | 42.7 | 1.6 | 0.9 | 2.3 | 60.0 | 133.0 | 0.7 | 22.0 | 0.0 |
ROC08 | 43.5 | 3.8 | 0.6 | 0.8 | 64.0 | 157.9 | 0.5 | 6.6 | 0.0 |
ROC09 | 35.7 | 3.1 | 2.6 | 3.4 | 590.0 | 828.8 | 420.0 | 7.5 | 0.0 |
DRASTIC index computation
The DRASTIC index was computed as suggested by Aller et al. (1987) without any adaptation of the usual method to the geological context or contaminant occurrence in groundwater in order to neutrally apply the method. Depth to groundwater (D) was estimated by subtracting piezometric levels from the digital elevation model (MRNF 2008). Where the bedrock aquifer is confined, D was set equal to the bedrock depth below unconsolidated sediments. Recharge (R) was determined with a spatially distributed water balance model with a 500 x 500 m resolution. Regional piezometric surface and spatially distributed recharge to the bedrock were established by Larocque et al. (2013). The bedrock aquifer lithology (A) was determined following Globensky (1987, 1993) who reports detailed local stratigraphy of the Cambro-Ordovician St. Lawrence Platform and the Appalachian Mountains. Soil type (S) was obtained from pedologic maps from IRDA (2012) which provides information about soil granulometry and drainage capacity to be classified with the usual DRASTIC ratings. The slope (T) was determined using a GIS treatment of a 10-m-cell resolution digital elevation model (MRNF 2008). The impact of the vadose zone (I) was determined from detailed Quaternary deposits maps from Godbout et al. (2011) which gives the type of granulometry associated with clay, silts, sands and glacial tills. Parameter (I) for the bedrock aquifer was determined from bedrock lithology description of Globensky (1987, 1993). Again, the classification here refers to soils or bedrock type obtained from available maps of the study area, and ratings were directly associated with the typical ratings as defined by Aller et al. (1987). The bedrock hydraulic conductivity (C) was set to the minimal ranking value of 1 for the entire study area since all field-measured hydraulic conductivities (Larocque et al. 2013) were lower than the threshold for C = 1.
Results and discussion
Regional hydrogeochemistry of the study area
Nitrate concentrations are relatively low, with a maximum value of 6.1 mg N–NO3/L (drinking water limit 10 mg N–NO3/L; MDDEFP 2013) and not related to the well depth (Table 1). These concentrations are similar to those reported in other studies from southern Quebec (MENV 2004). Eleven wells (12.9 %) mainly located in the Appalachian foothills have concentrations exceeding 1 mg/L N–NO3, i.e. the anthropogenic background (Dubrovsky et al. 2010). Low nitrate concentrations in the study area could be attributed to the high recharge rates (159 mm/year, Larocque et al. 2013) which might induce significant dilution within the aquifer (Andrade and Stigter 2009). They could also be due to the interception of nitrate-rich infiltrated water by agricultural drains (Qi et al. 2011) and to denitrification (see further discussion below).
Hydrogeochemical Vulnerability Index
The HVI can be derived similarly for other hydrogeological contexts where calcareous dissolution from recharge and Ca2+–Na+ cation exchange dominate the water chemistry. This is a common pattern regarding the evolution of groundwater geochemistry composition along the flow paths (Edmunds et al. 2003; Appelo and Postma 2005). The HVI is expected to be particularly suitable in glacial hydrogeomorphological contexts, i.e. with the presence of calcareous materials found in tills, in sedimentary bedrock rich in clay minerals, and under gradual confinement conditions of the fractured aquifer with marine clays. The HVI was tested in other regions of the St. Lawrence Lowlands in southern Quebec, taking advantage of newly available hydrogeochemical datasets from the Nicolet and lower Saint-François watersheds (Larocque et al. 2015) and in the Monteregie region (Carrier et al. 2013). For these regions, the HVI was built using the same ranges of Na–Cl (y-axis) and Ca + Mg–HCO3–SO4 (x-axis) than for the current study area. HVI values larger than 9 were found, respectively, for 83 % and 89 % of wells with detectable nitrate (above 0.1 mg N–NO3/L) (results not illustrated). In other geological and climatic contexts, it might be necessary to modify the axes ranges in Fig. 6 to adjust to site-specific conditions.
The HVI provides integrative information on well vulnerability while being relatively inexpensive to implement and easily computed. Similarly to other types of interpolated maps (e.g. maps of hydrogeological contexts interpolated from local drilling data), the uncertainty related to the interpolated surface is directly linked to data point density. For example, because municipal and private wells are rare near the St. Lawrence River (due to high-salinity groundwater), the vulnerability map in the lower portion of the study area is more uncertain. In the case of local groundwater contamination, the method may show heterogeneities related to anthropogenic sources (e.g. Na–Cl pollution from deicing road salts, surface water infiltration into a poorly maintained well cap) rather than to the natural hydrogeological context. A preliminary check was performed to discard results from samples impacted by deicing road salt.
DRASTIC vulnerability map
It is not possible to integrate complex sequences of overlying Quaternary deposits when using the DRASTIC index to estimate the vulnerability of a bedrock aquifer. This is due to the fact that it uses only two parameters for the overlying unconsolidated sediments, i.e. one parameter for soil type (S) and another parameter for the impact of the vadose zone (I). Recharge is usually the main vector for solute transport through the vadose zone, as highlighted in other studies the importance of recharge in aquifer vulnerability estimation (e.g. Rupert 2001; Nobre et al. 2007). This is reflected in the index, where four out of seven parameters relate to infiltration and recharge processes (R, S, T and I). On the DRASTIC map (Fig. 8), the most extended and vulnerable aquifer zones are found in the Appalachian Piedmont, while hydrogeochemistry suggests that recharge areas are predominantly located in the Appalachian Mountains. High aquifer vulnerability in this area is caused by high S and I parameters in the presence of sandy soils. However, these regressive and aeolian sands are often superficial deposits covering thick till deposits considered as aquitards. This underlying till probably offers a good protection for the underlying bedrock aquifer, which is not taken into account by the DRASTIC index.
Comparing HVI and DRASTIC indices
The two vulnerability indices (Figs. 7, 8) do not show the same spatial distribution because they do not integrate the same parameters and processes. The HVI reflects a cumulative sequence of hydrochemical processes, from the recharge area to the aquifer domain where renewal rates are low. It can be considered as an integrated representation of the flow lines from the recharge area to the pumped well and reflects borehole vulnerability. The DRASTIC index is mostly based on parameters responsible for vertical flows from the surface to the aquifer. It maps areas of potential groundwater contamination and does not consider flow line distribution or convergence of diffuse pollution to a well, nor does it take into account groundwater flow dynamics. For instance, an area covered with impermeable sediment will be associated with a low DRASTIC index, but groundwater at this site may be affected by contamination from neighbouring upstream vulnerable areas where recharge occurs.
Nitrate is highly leachable in groundwater and is often chosen to trace anthropogenic impacts on groundwater resource at regional scales (Rodriguez-Galiano et al. 2014). It is also the contaminant that most frequently exceeds drinking water standard (e.g. MENV 2004; US Environmental Protection Agency 2005; Stuart et al. 2007). However, because it is subject to denitrification, nitrate is not a perfect tracer. Previous studies detailed the processes involved for denitrification due to oxic/anoxic conditions (Korom 1992; McMahon et al. 2004) which are associated with microbial (McMahon et al. 2008) or pyrite reduction (Böhlke et al. 2002) in groundwater. In the current study, denitrification rates were not estimated because of the density of sampling locations and because of the relatively low nitrate concentrations measured.
Because both methods estimate the intrinsic vulnerability, neither considers the presence of a contaminant source at the surface and the difference between their capacities to identify the presence of nitrate in groundwater is linked to one of the other two causes. The better nitrate/vulnerability index correspondence for the HVI method is probably explained by the fact that major ions geochemistry reflects groundwater flow dynamics which are not considered in DRASTIC. The inclusion of parameters considered representative of water and contaminant flow through the unsaturated zone (i.e. A and C parameters) is not sufficient for DRASTIC to correlate better with nitrate concentrations. Using concentrations from other anthropogenic contaminants, performing tracer tests or groundwater flow modelling on the studied aquifer would provide a more complete test of the HVI method capacity to identify regional-scale groundwater vulnerability.
Conclusions
The objective of this work was to test the potential of major ion chemistry to assess groundwater vulnerability of a regional aquifer. A Hydrogeochemical Vulnerability Index (HVI) was developed and applied to a southern Quebec fractured bedrock aquifer, and results were compared to those of the classical DRASTIC method, and to measured nitrate concentrations.
Major ion of groundwater samples evolve from (Ca, Mg)–HCO3 type in recharge areas to Na–HCO3 and Na–Cl type downgradient in the aquifer. Although relatively low, nitrate concentrations in nearly 13 % of the sampled wells show anthropogenic impacts from agricultural activities. The proposed HVI integrates processes from both infiltration and groundwater flow dynamics and shows scores consistent with the occurrence of nitrate in groundwater. It also highlights local discontinuities in confinement that differ from the regional pattern. Eighty-two per cent of all groundwater samples with detectable nitrate groundwater show a HVI close to the maximum value of 10, a performance significantly better than that of the DRASTIC index. The poor correlation between the DRASTIC index and the nitrate concentration in groundwater is probably due to the fact that the DRASTIC method does not include groundwater flow dynamics.
This work suggests that major ion geochemistry contains significant information about intrinsic well vulnerability. This inexpensive and easily implemented method is transferrable to similar geological contexts, but would need to be adapted if carbonate dissolution and cation exchange are not the main hydrogeochemical processes which control the groundwater composition locally and regionally. It would also need to be tested using concentrations of other anthropogenic contaminants and confronted to other vulnerability methods. Combining the hydrogeochemical vulnerability method with the mapping of non-point sources of anthropogenic pollution at the land surface may contribute a real support tool for groundwater integrated resource-protecting policies.
Notes
Acknowledgments
We wish to thank an anonymous reviewer for his careful review and suggestions that greatly improved the manuscript. The authors thank the Quebec Ministry of Environment (Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques), the Becancour River Watershed organization (organisme de bassin versant GROBEC) and the municipalities that have participated in financing and supporting this research.
References
- Aller L, Bennett T, Lehr JH, Petty RJ, Hackett G (1987) DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeologic settings. EPA-600/2-87-035, 20 pGoogle Scholar
- Andrade AIASS, Stigter TY (2009) Multi-method assessment of nitrates and pesticides contamination in shallow alluvial groundwater as a function of hydrogeologic setting and land use. Agric Water Manag 96:1751–1765CrossRefGoogle Scholar
- Appelo CAJ, Postma D (2005) Geochemistry, groundwater and pollution, 2nd edn. A.A. Balkema Publishers, LeidenCrossRefGoogle Scholar
- Barbecot F, Marlin C, Gibert E, Dever L (2000) Hydrochemical and isotopic characterisation of the Bathonian and Bajocian coastal aquifer of the Caen area (northern France). Appl Geochem 15(6):791–805CrossRefGoogle Scholar
- Beaudry C (2013) Hydrogéochimie de l’aquifère rocheux régional en Montérégie est, Québec. MSc thesis, INRS-ETE, Université du Québec, Québec, Canada, 210 p. Access to the geochemical dataset online: http://sdis.inrs.ca/ (in French)
- Benoit N, Nastev M, Blanchette D, Molson J (2014) Hydrogeology and hydrogeochemistry of the Chaudière River watershed aquifers, Quebec, Canada. Can Water Resour J 39(1):32–48CrossRefGoogle Scholar
- Böhlke JK, Wanty R, Tuttle M, Delin G, Landon M (2002) Denitrification in the recharge area and discharge area of a transient agricultural nitrate plume in a glacial outwash sand aquifer, Minnesota. Water Resour Res 38:7. doi: 10.1029/2001WR000663 Google Scholar
- Bojórquez-Tapia A, Cruz-Bello GM, Luna-González L, Juárez L, Ortiz-Pérez MA (2009) V-DRASTIC: using visualization to engage policymakers in groundwater vulnerability assessment. J Hydrol 373:242–255CrossRefGoogle Scholar
- Carrier MA, Lefebvre R, Rivard C, Parent M, Ballard JM, Benoit N, Vigneault H, Beaudry C, Malet X, Laurencelle M, Gosselin JS, Ladevèze P, Thériault R, Beaudin I, Michaud A, Pugin A, Morin R, Crow H, Gloaguen E, Bleser J, Martin A, Lavoie D (2013) Portrait des ressources en eau souterraine en Montérégie Est, Québec, Canada. Projet réalisé conjointement par l’INRS, la CGC, l’OBV Yamaska et l’IRDA dans le cadre du Programme d’acquisition de connaissances sur les eaux souterraines, rapport final INRS R-1433, soumis en juin 2013, 312 p. (in French) Google Scholar
- CEAEQ (Centre d’expertise en analyse environnementale du Québec) (2014) Les méthodes d’analyses en usage au Centre d’expertise en analyse environnementale. On-line: http://www.ceaeq.gouv.qc.ca/methodes/methode_index.htm
- Champagne L (1990) Vulnérabilité des eaux souterraines à la pollution: M.R.C. de Montcalm. MSc thesis, Université de Montréal, Québec, Canada, 80 p (in French) Google Scholar
- Charron JE (1978) Hydrogeochemical study of groundwater flow in the interstream area between the Ottawa and St. Lawrence Rivers. Environment Canada, Water Resource Branch, Scientific series No. 76, 45 pGoogle Scholar
- Chen SK, Jang CS, Peng YP (2013) Developing a probability-based model of aquifer vulnerability in an agricultural region. J Hydrol 486:494–504CrossRefGoogle Scholar
- Chowdhury SH, Kehew AE, Passero RN (2003) Correlation between nitrates contamination and groundwater pollution potential. Groundwater 41:735–745CrossRefGoogle Scholar
- Cloutier V, Lefebvre R, Therrien R, Savard MM (2008) Multivariate statistical analysis of geochemical data as indicative of the hydrogeochemical evolution of groundwater in a sedimentary rock aquifer system. J Hydrol 353:294–313CrossRefGoogle Scholar
- Cloutier V, Lefebvre R, Therrien R, Savard MM (2010) Desalination of a sedimentary rock aquifer system invaded by Pleistocene Champlain Sea water and processes controlling groundwater geochemistry. Environ Earth Sci 59:977–994CrossRefGoogle Scholar
- Dubrovsky NM, Burow KR, Clark GM, Gronberg JM, Hamilton PA, Hitt KJ, Mueller DK, Munn MD, Nolan BT, Puckett LJ, Rupert MG, Short TM, Spahr NE, Sprague LA, Wilber WG (2010) The quality of our Nation’s waters—nutrients in the Nation’s streams and groundwater, 1992–2004. U.S. Geological Survey Circular, vol 1350, 174 pGoogle Scholar
- Edmunds WM, Cook JM, Darling WG, Kinniburgh DG, Miles DL, Bath AH, Morganjones M, Andrews JN (1987) Baseline geochemical conditions in the Chalk aquifer, Berkshire, U.K: a basis for groundwater quality management. Appl Geochem 2:251–274CrossRefGoogle Scholar
- Edmunds WM, Shand P, Hart P, Ward RS (2003) The natural (baseline) quality of groundwater: a UK pilot study. Sci Total Environ 310:25–35CrossRefGoogle Scholar
- Environment Canada (2012) Canadian climate normals 1971–2000 for Laurierville, Québec. http://climate.weather.gc.ca/climate_normals/index_e.html
- EPA (Environmental Protection Agency) (1993) A review of methods for assessing aquifer sensitivity and groundwater vulnerability to pesticide contamination. EPA-813-R-93002, 147 pGoogle Scholar
- Frind EO, Molson JW, Rudolph DL (2006) Well vulnerability: a quantitative approach for source water protection. Groundwater 44:732–742Google Scholar
- Globensky Y (1987) Géologie des Basses-Terres du Saint-Laurent, Québec. Ministère des Richesses Naturelles du Québec 63 (v. MM 85-02) (in French) Google Scholar
- Globensky Y (1993) Lexique stratigraphique canadien. Volume V-B: région des Appalaches, des Basses-Terres du Saint-Laurent et des Iles de la Madeleine. Ministère de l’Énergie et des Ressources et Direction Générale de l’Exploration géologique et minérale, p. 327, DV 91e23 (in French) Google Scholar
- Godbout PM, Lamothe M, Horoi V, Caron O (2011) Synthèse stratigraphique, cartographie des dépôts quaternaires et modèle hydrostratigraphique régional, secteur de Bécancour, Québec: Rapport final. Report presented to the Ministère des Ressources naturelles, 37 p (in French) Google Scholar
- IRDA (Institut de recherche et de développement en agroenvironnement) (2012) Feuillets pédologiques numériques 1:20000: 21L12, 21L06, 21L05, 21L04, 21L03, 31I09, 31I08, 31I07, 31I02, 31I01Google Scholar
- Korom SF (1992) Natural denitrification in the saturated zone: a review. Water Resour Res 28:1657–1668CrossRefGoogle Scholar
- Kozuskanich JC, Novakowski KS, Anderson BC, Crowe AS, Balakrishnan YK (2014) Anthropogenic impacts on a bedrock aquifer at the village scale. Groundwater 52:474–486CrossRefGoogle Scholar
- Lamothe M (1989) A new framework for the Pleistocene stratigraphy of the central St. Lawrence Lowland, southern Quebec. Géogr Phys Quart 43:119–129Google Scholar
- Larocque M, Gagné S, Tremblay L, Meyzonnat G (2013) Projet de connaissance des eaux souterraines du bassin versant de la rivière Becancour et de la MRC de Becancour—Rapport scientifique. Report submitted to the Ministère du Développement durable, de l’Environnement, de la Faune et des Parcs, 213 p (in French) Google Scholar
- Larocque M, Gagné S, Barnetche D, Meyzonnat G, Graveline MH, Ouellet MA (2015) Projet de connaissance des eaux souterraines du bassin versant de la zone Nicolet et de la partie basse de la zone Saint-François. Rapport scientifique. Report submitted to the Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques, 260 p (in French) Google Scholar
- Mansoor A, Baloch MA, Sahar L (2014) Development of a watershed-based geospatial groundwater specific vulnerability assessment tool. Groundwater 52:137–147CrossRefGoogle Scholar
- McMahon PB, Böhlke JK, Christenson SC (2004) Geochemistry, radiocarbon ages, and paleorecharge conditions along a transect in the central High Plains aquifer, southwestern Kansas, USA. Appl Geochem 19:1655–1686CrossRefGoogle Scholar
- McMahon PB, Böhlke JK, Kauffman LJ, Kipp KL, Landon MK, Crandall CA, Burow KR, Brown CJ (2008) Source and transport controls on the movement of nitrate to public supply wells in selected principal aquifers of the United States. Water Resour Res 44:W04401. doi: 10.1029/2007WR006252 Google Scholar
- MDDEFP (Ministère du Développement durable, de l’Environnement, de la Faune et des Parcs) (2013) Règlement sur la qualité de l’eau potable. c. Q-2, r. 40. http://www2.publicationsduquebec.gouv.qc.ca/ (in French)
- Mehnert E, Keefer DA, Dey WS, Wehrmann HA, Wilson SD, Ray C (2005) Aquifer sensitivity to pesticide leaching: testing a soil and hydrogeologic index method. Groundw Monit Remediat 25:60–67CrossRefGoogle Scholar
- Mendizabal I, Stuyfzand PJ (2011) Quantifying the vulnerability of well fields towards anthropogenic pollution: The Netherlands as an example. J Hydrol 398:260–276CrossRefGoogle Scholar
- MENV (Ministère de l’Environnement) (2004) Étude de la qualité de l’eau potable dans sept bassins versants en surplus de fumier et impacts potentiels sur la santé. Ministère de l’Environnement du Québec, Québec, Canada, Envirodoq ENV/2004/0312, 137 p (in French) Google Scholar
- MRNF (Ministère des Ressources naturelles et de la Faune) (2008) Digital elevation model 1:20000, 21L12, 21L06, 21L05, 21L04, 21L03, 31I09, 31I08, 31I07, 31I02, 31I01. Ministère des Ressources naturelles et de la FauneGoogle Scholar
- Murat V (2000) Étude comparative des méthodes d’évaluation de la vulnérabilité intrinsèque des aquifères à la pollution: application aux aquifères granulaires du piémont Laurentien. MSc thesis, INRS-ETE, Université du Québec, Québec, Canada, 291 p (in French) Google Scholar
- Nobre RCM, Filho OCR, Mansur WJ, Consenza CAN (2007) Groundwater vulnerability and risk mapping using GIS modeling and and a fuzzy logic tool. J Contam Hydrol 94:277–292CrossRefGoogle Scholar
- Qi Z, Helmers MJ, Christianson RD, Pederson CH (2011) Nitrate-nitrogen losses through subsurface drainage under various agricultural land covers. J Environ Qual 40:1578–1585CrossRefGoogle Scholar
- Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive modeling of groundwater nitrate pollution using random forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain). Sci Total Environ 476–477:189–206CrossRefGoogle Scholar
- Rupert MG (2001) Calibration of the DRASTIC groundwater vulnerability mapping method. Groundwater 39:625–630CrossRefGoogle Scholar
- Saeedi M, Abessi O, Sharifi F, Meraji H (2009) Development of groundwater quality index. Environ Monit Assess 163:327–335CrossRefGoogle Scholar
- Saidi S, Bouri S, Ben Dhia H (2010) Groundwater vulnerability and risk mapping of the Hajeb-jelma aquifer (Central Tunisia) using a GIS-based DRASTIC model. Environ Earth Sci 59:1579–1588CrossRefGoogle Scholar
- Saxena V, Ahmed S (2001) Dissolution of fluoride in groundwater: a water-rock inter-action study. Env Geology 40:1084–1087CrossRefGoogle Scholar
- Sorichetta A, Ballabio C, Masetti M, Robinson GR, Sterlacchini S (2013) A comparison of data-driven groundwater vulnerability assessment methods. Groundwater 51:866–879CrossRefGoogle Scholar
- Stigter TY, Ribeiro L, Carvalho Dill AMM (2008) Building factorial regression models to explain and predict nitrates concentrations in groundwater under agricultural land. J Hydrol 357:42–56CrossRefGoogle Scholar
- Stuart ME, Chilton PJ, Kinniburgh DG, Cooper DM (2007) Screening for long-term trends in groundwater nitrate monitoring data. Q J Eng Geol Hydrogeol 40:361–376CrossRefGoogle Scholar
- Tilahun K, Merket BJ (2010) Assessment of groundwater vulnerability to pollution in Dire Dawa, Ethiopia using DRASTIC. Environ Earth Sci 59:1485–1496CrossRefGoogle Scholar
- United Nations Environment Programme (2003) Groundwater and its susceptibility to degradation: a global assessment of the problem and options for management, ISBN: 92-807-2297-2, 140 pGoogle Scholar
- US Environmental Protection Agency (2005) Factoids: drinking water and ground water statistics for 2004. EPA 816-K-05-001, 15 pGoogle Scholar
- Van Stempvoort D, Ewert L, Wassenaar L (1992) AVI: a method for groundwater protection mapping in the Prairie. Provinces of Canada, Prairie Provinces Water Board Report No. 114Google Scholar
- Vrba J, Zoporozec A (1994) Guidebook on mapping groundwater vulnerability. IAH International Contribution for Hydrogeology, Hannover, Germany, 160 pGoogle Scholar
- Yu C, Yao Y, Cao G, Zheng C (2014) A field demonstration of groundwater vulnerability assessment using transport modeling and groundwater age modeling, Beijing Plain, China. Environ Earth Sci. doi: 10.1007/s12665-014-3769-5 Google Scholar
- Zhou J, Li Q, Guo Y, Guo X, Li X, Zhao Y, Jia R (2012) VLDA model and its application in assessing phreatic groundwater vulnerability: a case study of phreatic groundwater in the plain area of Yanji County, Xinjiang, China. Environ Earth Sci 67:1789–1799CrossRefGoogle Scholar