Decadal variations in groundwater quality: A legacy from nitrate leaching and denitrification by pyrite in a sandy aquifer

Abstract

Twenty‐five years of groundwater quality monitoring in a sandy aquifer beneath agricultural fields showed large temporal and spatial variations in major ion groundwater chemistry, which were linked closely to the nitrate (NO3) content of agricultural recharge. Between 1988 and 2013, the NO3 content of water in the oxidized zone of the aquifer nearly halved, following implementation of action plans to reduce N leaching from agriculture. However, due to denitrification by pyrite oxidation in the aquifer, a plume of sulfate‐rich water migrates through the aquifer as a legacy of the historical NO3 loading. Agriculture thus is an important determinant of major ion groundwater chemistry. Temporal and spatial variations in the groundwater quality were simulated using a 2D reactive transport model, which combined effects of the historical NO3 leaching and denitrification, with dispersive mixing into the pristine groundwater residing deeper in the aquifer. Reactant‐to‐product ratios across reaction fronts are altered by dispersive mixing and transience in reactant input functions. Modelling therefore allowed a direct comparison of observed and simulated ratios of concentrations of NO3 (reactant) in the oxidized zone to those of SO4 (product) in the reduced zone, which aided a stoichiometric assessment of the mechanisms of denitrification. Denitrification by pyrite in the Rabis Creek aquifer results in oxidation of S−1 and Fe2+ in pyrite to S6+ in dissolved SO4 and Fe3+ in Fe‐oxide. Neither precipitation of elemental sulfur (S0), nor of jarosite, was supported by observations, and adsorption of sulfate was also dismissed.

Microbial anaerobic Fe(II) oxidation – Ecology, mechanisms and environmental implications

“…Firstly, the ability of Fe(II)-oxidizers to remove nitrate may be harnessed for the improvement of drinking water, waste water and sludge in sewage treatment plants (Davidson et al, 2003;Zhang et al, 2015;Wang et al, 2016a;Kiskira et al, 2017). This may already occur naturally in aquifers where Fe(II)-oxidizing bacteria could couple nitrate reduction to oxidation of Fe(II)-rich clays or Fe(II)-containing minerals such as pyrite (Haaijer et al, 2007;Vaclavkova et al, 2015;Jessen et al, 2017). There is also potential to use minerals produced by these anaerobic Fe(II)-oxidizers, particularly reactive Fe(II)-Fe(III) phases like green rust or magnetite in remediation of metals and contaminants (reviewed in Usman et al, 2018).…”

Saltwater intrusion affects nutrient concentrations in soil porewater and surface waters of coastal habitats

“…First, NO 3 -N concentrations measured in our samples were mostly at or near the method detection limit (<0.025 mg NO 3 -N/L). Since NO 3 À is prone to leaching, particularly in sandy soils (Clarke et al 2002) like those of our study habitats (Table 2), some NO 3 À may have leached into deeper soil layers or groundwater below the depth of our porewater samples (Clarke et al 2002, Tanner and Sukias 2011, Jessen et al 2017. Second, as these systems are frequently inundated, NO 3 À Fig.…”

“…, Tanner and Sukias , Jessen et al. ). Second, as these systems are frequently inundated, NO 3 − may also be converted to gaseous forms during denitrification (Burgin and Hamilton , Helton et al.…”

Detecting dominant changes in irregularly sampled multivariate water quality data sets

“…PCA is one of the most widespread dimension-reduction methods going back to research of Pearson (1901) and Hotelling (1933). For a brief introduction to PCA, please see for example Jolliffe and Cadima (2016) and for a comprehensive one see Jolliffe (2002). PCA aims (25) GdP_201 (25) GsP_202 (11) GdQ_198 (28) GsQ_199 (18) GsP_200 (6) GdQ_204 (25) GdQ_205 (2) P_110 (51) P_109 (8) P_108 (61) P_107 (78) Q_103 (8) Q_102 (11) Q_106 (12) Q_104 (71) Q_100 (110) Q_98 (127) Q_97 (126) Q_96 (11) Q_95 (125) Q_93 (126) S_122 (1) S_121 (23) S_120 (1) S_118 (118) St_133 (124) U_128 (114)…”

“…For PCA and Isomap, the first component represents by definition the correlation structure that predominantly can be extracted from the set of variables as a whole. If all the loadings of the first component of a PCA have the same sign, it is a weighted average of all the analysed variables (Jolliffe, 2002;Jolliffe and Cadima, 2016). The stronger the analysed variables are linearly correlated, the more the first component approximates the arithmetic mean of all variables (for examples with hydrometric data see Lischeid, et al, 2010;Lehr et al, 2015).…”