Vertical distribution and source identification of polycyclic aromatic hydrocarbons in anoxic sediment cores of Chini Lake, Malaysia: Perylene as indicator of land plant-derived hydrocarbons

Four anoxic sediment cores were collected from Chini Lake, Malaysia in order to investigate the variability of polycyclic aromatic hydrocarbon (PAH) and perylene concentrations. The study also determined significant differences of perylene concentrations in different sediment layers. Total PAH concentrations ranged from 248 to 8098 ng g⁻¹ in the samples. Diagnostic PAH ratios such as methylphenanthrenes/phenanthrene (MP/P), phenanthrene/anthracene (P/A) and fluoranthene/(fluoranthene + pyrene) (Fl/(Fl + Py) revealed a dominance of pyrogenic influences and partial petrogenic inputs to the top sediment layers. Perylene concentrations were high in the top layers (<12 cm) and increased with increasing depth. There is a significant positive correlation (r = 0.705, p = 0.01) between perylene concentrations and TOC. Analysis of variance (ANOVA) and LSD revealed significant differences (p < 0.05) in TOC-normalized perylene concentrations between the upper (<12 cm) and bottom layers (>12 cm). The average perylene concentrations accounted for 26–50% (0–12 cm) and 50–77% (12–36 cm) of pentacyclic-aromatic hydrocarbon isomers (PAI) present whereas it made up 10–34% (0–12 cm) and 46–66% (12–36 cm) of the total PAH. The average pyrene concentrations decreased with increasing depth and accounted for 62% (0–3 cm), 20–23% (3–12 cm) and 3–1.4% (12–36 cm) of perylene present. The results of hierarchical cluster analysis based on these ratios suggested different input sources for the top and bottom layers. It is concluded that the activity of termites on woody plants produced perylene which is supplied to the lake by run-off from the heavy and frequent rains in this Asian tropical climate. In addition, there was also in situ formation of perylene in the bottom layers due to diagenetic processes.     

Geochemistry of subsurface Late Quaternary ironstones in Rajshahi and Bogra Districts,Bangladesh: implications for genetic and depositional conditions

The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of the studied boreholes are clay, silty clay, sandy clay, fine to coarse grained sand, gravels and sands with (fragmentary) ironstones. The ironstones contain major oxides, Fe₂O₃* (* total Fe) (avg. 66.6 wt%), SiO₂ (avg. 15.3 wt%), Al₂O₃ (avg. 4.0 wt%), MnO (avg. 7.7 wt%), and CaO (avg. 3.4 wt%). These geochemical data imply that the higher percentage of Fe₂O₃* along with Al₂O₃ and MnO indicate the ironstone as goethite and siderite, which is also validated by XRD data. A comparatively higher percentage of SiO₂ indicates the presence of relative amounts of clastic quartz and manganese-rich silicate or clay in these rocks. These ironstones also have significant amounts of MnO (avg. 7.7 wt%) suggesting their depositional environments under oxygenated condition. Chemical data of these ironstones suggest that the source rock suffered deep chemical weathering and iron was mostly carried in association with the clay fraction and organic matter. Iron concretion was mostly formed by bacterial build up in swamps and marshes, and was subsequently embedded in clayey mud. Within the coastal environments, the water table fluctuates and goethite and siderite with mud and quartz became dry and compacted to form ironstone.

     

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer,Iran

Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze \( {\text{NO}}_{ 3}^{ - } \) concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the \( {\text{NO}}_{3}^{ - } \) data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using \( {\text{NO}}_{3}^{ - } \) concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and \( {\text{NO}}_{3}^{ - } \) concentration values. After model training, the AI models are verified by the second \( {\text{NO}}_{3}^{ - } \) concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.     

Origin of brine in the Kangan gasfield: isotopic and hydrogeochemical approaches

The Kangan Permo-Triassic brine aquifer and the overlying gas reservoir in the southern Iran are located in Kangan and Dalan Formations, consisting dominantly of limestone, dolomite, and to a lesser extent, shale and anhydrite. The gasfield, 2,900 m in depth and is exploited by 36 wells, some of which produce high salinity water. The produced water gradually changed from fresh to saline, causing severe corrosion in the pipelines and well head facilities. The present research aims to identify the origin of this saline water (brine), as a vital step to manage saline water issues. The major and minor ions, as well as δ²H, δ¹⁸O and δ³⁷Cl isotopes were measured in the Kangan aquifer water and/or the saline produced waters. The potential processes causing salinity can be halite dissolution, membrane filtration, and evaporation of water. The potential sources of water may be meteoric, present or paleo-seawater. The Na/Cl and I/Cl ratios versus Cl^? concentration preclude halite dissolution. Concentrations of Cl, Na, and total dissolved solid were compared with Br concentration, indicating that the evaporated ancient seawater trapped in the structure is the cause of salinization. δ¹⁸O isotope enrichment in the Kangan aquifer water is due to both seawater evaporation and interaction with carbonate rocks. The δ³⁷Cl isotope content also supports the idea of evaporated ancient seawater as the origin of salinity. Membrane filtration is rejected as a possible source of salinity based on the hydrochemistry data, the δ¹⁸O value, and incapability of this process to dramatically enhance salinity up to the observed value of 330,000 mg/L. The overlaying impermeable formations, high pressure in the gas reservoir, and the presence of a cap rock above the Kangan gasfield, all prevent the downward flow of meteoric and Persian Gulf waters into the Kangan aquifer. The evaporated ancient seawater is autochthonous, because the Kangan brine aquifer was formed by entrapment of brine seawater during the deposition of carbonates, gypsum, and minor clastic rocks in a lagoon and sabkha environment. The reliability of determining the source of salinity in a deep complicated inaccessible high-pressure aquifer can be improved by combining various methods of hydrochemistry, isotope, hydrodynamics, hydrogeology and geological settings.     

Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model

The use of electrical conductivity (EC) as a water quality indicator is useful for estimating the mineralization and salinity of water. The objectives of this study were to explore, for the first time, extreme learning machine (ELM) and wavelet-extreme learning machine hybrid (WA-ELM) models to forecast multi-step-ahead EC and to employ an integrated method to combine the advantages of WA-ELM models, which utilized the boosting ensemble method. For comparative purposes, an adaptive neuro-fuzzy inference system (ANFIS) model, and a WA-ANFIS model, were also developed. The study area was the Aji-Chay River at the Akhula hydrometric station in Northwestern Iran. A total of 315 monthly EC (µS/cm) datasets (1984–2011) were used, in which the first 284 datasets (90% of total datasets) were considered for training and the remaining 31 (10% of total datasets) were used for model testing. Autocorrelation function (ACF) and partial autocorrelation function (PACF) demonstrated that the 6-month lags were potential input time lags. The results illustrated that the single ELM and ANFIS models were unable to forecast the multi-step-ahead EC in terms of root mean square error (RMSE), coefficient of determination (R²) and Nash–Sutcliffe model efficiency coefficient (NSC). To develop the hybrid WA-ELM and WA-ANFIS models, the original time series of lags as inputs, and time series of 1, 2 and 3 month-step-ahead EC values as outputs, were decomposed into several sub-time series using different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Coiflet of different orders at level three. These sub-time series were then used in the ELM and ANFIS models as an input dataset to forecast the multi-step-ahead EC. The results indicated that single WA-ELM and WA-ANFIS models performed better than any ELM and ANFIS models. Also, WA-ELM models outperformed WA-ANFIS models. To develop the boosting multi-WA-ELM and multi-WA-ANFIS ensemble models, a least squares boosting (LSBoost) algorithm was used. The results showed that boosting multi-WA-ELM and multi-WA-ANFIS ensemble models outperformed the individual WA-ELM and WA-ANFIS models.     

Geochemistry of subsurface Late Quaternary ironstones in Rajshahi and Bogra Districts,Bangladesh: implications for genetic and depositional conditions

The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of the studied boreholes are clay, silty clay, sandy clay, fine to coarse grained sand, gravels and sands with(fragmentary) ironstones. The ironstones contain major oxides, Fe_2 O_3*(*total Fe)(avg. 66.6 wt%), SiO_2(avg. 15.3 wt%), Al_2 O_3(avg. 4.0 wt%), MnO(avg. 7.7 wt%), and CaO(avg. 3.4 wt%). These geochemical data imply that the higher percentage of Fe_2 O_3* along with Al_2 O_3 and MnO indicate the ironstone as goethite and siderite, which is also validated by XRD data. A comparatively higher percentage of SiO_2 indicates the presence of relative amounts of clastic quartz and manganese-rich silicate or clay in these rocks. These ironstones also have significant amounts of MnO(avg. 7.7 wt%) suggesting their depositional environments under oxygenated condition. Chemical data of these ironstones suggest that the source rock suffered deep chemical weathering and iron was mostly carried in association with the clay fraction and organic matter. Iron concretion was mostly formed by bacterial build up in swamps and marshes, and was subsequently embedded in clayey mud.Within the coastal environments, the water table fluctuates and goethite and siderite with mud and quartz became dry and compacted to form ironstone.     

Etiology of Salinity and Water Origin,the Main Dilemma of Badab Sourt,a Unique Travertine Spring

Badab Sourt travertine‐depositing springs in the north of Iran, naturally create a unique surreal landscape containing a range of stepped travertine terraces, similarly found only in a few other places on earth. This site comprises of three travertine saline springs with different values of salinity and discharge (SP₁, SP₂, and SP₃) and one non‐travertine fresh karstic spring (SP₄) within a distance of about 300 m. The etiology behind this salinity and the water origin are the main research's dilemma that were investigated using geological, hydrochemical, and stable isotopic techniques. Based on the topography and isotopic results, the carbonate formations in northern (Khoshyeilagh and Mobarak) and southern (Cretaceous limestone) parts of the springs potentially provide the initial hydraulic gradient for deep circulation of the water and CO₂. However, geological studies indicate that the hydraulic connectivity of the Cretaceous formation to the travertine springs is interrupted by impermeable geological formations. Based on the proposed conceptual hydrogeological model and mass balance calculations, the SP₄ spring is locally recharged from the nearby karstic area of Khoshyeilagh formation through shallow, short and steep groundwater flow circulation that is completely different from the travertine springs. The travertine spring (SP₁) is recharged from more distant areas having higher altitudes on Mobarak and Khoshyeilagh limestone and circulate more deeply before emerging on the surface. The SP₂ and SP₃ springs can derive from the mixing of the saline water (SP₁) and fresh water (SP₄). The dissolution of interlayers of halite in Shemshak formation is concluded as the main source of salinity. This is the first research article in detail to survey hydrogeology of the travertine springs in Iran.