Identification of groundwater nitrate sources in an urban aquifer (Alborz Province, Iran) using a multi-parameter approach.

High concentrations of NO3̄ in water resources are detrimental to both human health and aquatic ecosystems. Identification of NO3̄ sources and biogeochemical processes is a crucial step in managing and controlling NO3̄ pollution. In this study, land use, hydrochemical data, dual stable isotopic ratios and Bayesian Stable Isotope Mixing Models (BSIMM) were integrated to identify NO3̄ sources and estimate their proportional contributions to the contamination of the Karaj Urban Aquifer (Iran). Elevated NO3̄ concentrations indicated a severe NO3̄ pollution, with 39 and 52% of groundwater (GW) samples displaying the concentrations of NO3̄ in exceedance of the World Health Organization (WHO) standard of 50 mg NO3̄ L-1 in the rainy and dry seasons, respectively. Dual stable isotopes inferred that urban sewage is the main NO3̄ source in the Karaj Plain. The diagram of NO3̄/Cl‾ versus Cl‾ confirmed that municipal sewage is the major source of NO3̄. Results also showed that biogeochemical nitrogen dynamics are mainly influenced by nitrification, while denitrification is minimal. The BSIMM model suggested that NO3̄ originated predominantly from urban sewage (78.2%), followed by soil organic nitrogen (12.2%), and chemical fertilizer (9.5%) in the dry season. In the wet season, the relative contributions of urban sewage, soil nitrogen and chemical fertilizer were 87.5, 6.7, and 5.5%, respectively. The sensitivity analysis for the BSIMM modeling indicates that the isotopic signatures of sewage had the major impact on the overall GW NO3̄ source apportionment. The findings provide important insights for local authorities to support effective and sustainable GW resources management in the Karaj Urban Aquifer. It also demonstrates that employing Bayesian models combined with multi-parameters can improve the accuracy of NO3̄ source identification.

Hydrogeochemical and isotopic evolution of groundwater in shallow and deep aquifers of the Kabul Plain, Afghanistan.

Groundwater from shallow and deep aquifers are widely used for drinking, agricultural and industrial use in Kabul, the capital of Afghanistan. However, unplanned urbanization and rapid population growth has led to the installation of numerous unlicensed wells to meet the public demand. This has caused to extraction of huge amounts of groundwater from the subsurface and further deterioration of groundwater quality. Therefore, understanding the hydrogeochemical characteristics of groundwater in shallow aquifers and deep aquifers is imperative for sustainable management of the groundwater resource in Kabul Plain. Thus, in this study, we used a multi-parameter approach, involving hydrochemical and environmental isotopes to understand the geochemical evolution of entire groundwater system of the Kabul Plain including river and dam water. The results of this study show that shallow and deep aquifers are dominantly of Mg-(Ca)-HCO3 and Na-Cl water type, respectively. We observed that (1) water-rock interaction is the major contributing factor to the chemical compositions of groundwater in the Kabul Plain; (2) groundwater in deep aquifer is mainly influenced by silicate weathering, and dissolution of evaporitic and carbonate minerals and reverse cation exchange; (3) dissolution of carbonates and silicate weathering plays a pivotal role in the groundwater chemistry of shallow aquifer; (4) the stable isotopes of groundwater display that the shallow aquifer is principally recharged by river water and local precipitation; (5) the tritium analysis exhibited that groundwater of shallow aquifer was primarily recharged recently, whereas groundwater of deep aquifer is the mixture of pre 1953 with post 1953 groundwater. This study revealed that there are hydraulic interactions between the two aquifers and the deep aquifer is recharged through shallow aquifer. The findings of this study would be useful for Afghanistan's water authorities to develop an effective strategy for sustainable water resources management in the Kabul Basin.

Determining nitrate pollution sources in the Kabul Plain aquifer (Afghanistan) using stable isotopes and Bayesian stable isotope mixing model.

The Kabul urban aquifer (Afghanistan), which is the main source of drinking water for Kabul city's inhabitants, is highly vulnerable to anthropogenic pollution. In this study, the geochemistry of major ions (including reactive nitrogen species such as NO3-, NO2-, and NH4+) and stable isotope ratios (δ15N-NO3-, δ18O-NO3-, δ18O-H2O, and δ2H-H2O) of surface and groundwater samples from the Kabul Plain were analyzed over two sampling periods (dry and wet seasons). A Bayesian stable isotope mixing model (BSIMM) was also employed to trace potential nitrate sources, transformation processes, and proportional contributions of nitrate sources in the Kabul aquifer. The plotting of δ15N-NO3- against δ18O-NO3̄ (δ15N-NO3- and δ18O-NO3- values ranged from +4.8 to +25.4‰ and from -11.7 to +18.6‰, respectively) suggests that NO3- primarily originated from the nitrification of sewage rather than artificial fertilizer. The plotting of δ15N-NO3- versus NO3-/Cl- ratios also supported the assumption that sewage is the dominant nitrate source. The results indicate that denitrification did not influence the NO3- isotopic composition in the Kabul aquifer. The BSIMM model suggests that nitrate in the dry season originated mainly from sewage (~81%), followed by soil organic N (10.5%), and chemical fertilizer (8.5%). In the wet season, sewage (~87.5%), soil organic N (6.7%), and chemical fertilizer (5.8%) were the main sources of NO3- in the Kabul aquifer. Effective land management measures should be taken to improve the sewage collection system in the Kabul Plain.

Identifying sources of groundwater salinity and major hydrogeochemical processes in the Lower Kabul Basin aquifer, Afghanistan.

The Kabul Basin aquifer is the main source of water for domestic, agricultural and industrial use in Kabul city. Identifying the main hydrogeochemical processes and source of salinity is crucial for groundwater management. In this study, the results of 41 groundwater samples from the Lower Kabul Basin (LKB) aquifer were evaluated using major hydrogeochemical processes and hierarchical cluster analysis (HCA) methods. The electrical conductivity (EC) concentrations ranged from 672 to 15 290 µS cm-1 with a mean value of 1428 µS cm-1. The high spatial variations of EC in the LKB aquifer are due to different sources of salinity in groundwater. The results show that the Mg-(Ca, Na)-HCO3 water type is the dominant hydrogeochemical facies in the aquifer, followed by Mg-(Na)-Cl-(SO4), Na-(Mg)-SO4-(Cl), Na-Cl and Mg-Cl water types. The major factors controlling groundwater chemistry in the aquifer are the dissolution of carbonate, gypsum anhydrate minerals, weathering of silicates, ion exchange and mixing. Based on Cl/Br ratios, dissolution of minerals, anthropogenic and urban effects, and evaporitic lacustrine deposits are the possible sources of salinity in the aquifer. According to Cl/Br ratios and hierarchical cluster analysis, the evaporitic lacustrine deposits are the main source of salinity in the aquifer. The new groundwater survey also confirms the findings. The Cl/Br ratios and HCA successfully identified the sources of salinity in the LKB aquifer. The results of this study can provide a basis for local decision makers to develop effective and sustainable groundwater resources and environmental management strategies for Kabul city.

A proposed modelling towards the potential impacts of climate change on a semi-arid, small-scaled aquifer: a case study of Iran.

Several studies have evaluated the impact of climate change on the alluvial aquifers; however, no research has been carried out on a small-scale aquifer without any human influences and pumping wells. The object of this study is to assess the response of such an aquifer to the climate change to observe if it can preserve its storage or not. Pali aquifer, southwest Iran, is solely discharged by Taraz-Harkesh stream and geological formations. On the other hand, it is recharged by precipitation and geological formations. The Taraz-Harkesh stream's discharge rates and the Pali aquifer's groundwater level were simulated by IHACRES and MODFLOW, respectively, in the base (1961-1990) and future (2021-2050) time periods under two Representative Concentration Pathways, i.e., RCP4.5 and RCP8.5. The outputs of IHACRES were regarded as the input of MODFLOW. The groundwater model was calibrated in the steady-state for the hydrological year 2007 and in the unsteady-state for the time period 2008-2014 with annual time steps. Further, the groundwater model was verified for the time period 2015-2018. The statistical criteria maintained the groundwater model's ability, consequently measuring the root mean square error to be 0.69, 0.85, and 1.18 m for the steady calibration, unsteady calibration and verification of the groundwater model, respectively. Results indicate that the stream's discharge rates would decrease in the future time period, especially under RCP8.5. Nevertheless, the groundwater level would not fluctuate considerably. Indeed, the groundwater resources, even a semi-arid, small-scaled aquifer, may be considered as the water supplying systems under the future climate change.

Prediction of the karstic spring flow rates under climate change by climatic variables based on the artificial neural network: a case study of Iran.

Few studies have evaluated the impact of climate change on groundwater resources for a region with no pumping well. Indeed, the uncertainty of pumping wells may undesirably influence the results. Therefore, a region without any pumping well was selected to assess the impact of climate change on the karstic spring flow rates. NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset was used to extract the climatic variables for the present (1961-1990) and future (2021-2050) time periods by two Representative Concentration Pathways (RCPs), i.e., RCP4.5 and RCP8.5, in Lali region, southwest Iran. Although this dataset has been already verified, its output was evaluated for Lali region. Then, the impact of climate change on the discharge of Bibitarkhoun karstic spring was examined by the Artificial Neural Network (ANN). In this regard, if considering the daily data, ANN is not trained satisfactorily, because of the spring's lag time response to the precipitation; if monthly time step is considered, the data would not be adequate. Therefore, the average of some previous days was considered to calculate the variables. The average precipitation is 344, 329, and 324 mm/year and the average temperature is 14.18, 15.98, and 16.3 °C both for the present, future time period under RCP4.5 and future time period under RCP8.5, respectively. The network selected demonstrated no climate change impact on the average of spring discharge. However, the discharge increased by about + 8% in spring and summer and decreased by about - 7% in autumn and winter in the future time period.