Major-Ion Chemistry and Quality of Water in Rivers of Northern West Siberia

This study reports a synthesis of years-long hydrogeochemical monitoring in northern West Siberia, performed by the Russian Meteorological Service (Rosgidromet) and several academic institutions. Natural factors and intensive human economic activity lead to the disruption of the ecosystems of the northern territories of Western Siberia. The aim of this study is to estimate the background water chemistry parameters in the rivers of northern West Siberia in the beginning of the 21st century. The mean values hydrochemical and geochemical indicators were determined with STATISTICA software, which can be used as background values in assessing the actual and allowable anthropogenic impact on water bodies. We revealed four water chemistry provinces: western Ob Gulf and Ob estuary catchments (I); eastern Ob Gulf and Taz Gulf catchments, except for the Taz River and its tributaries (II); Taz River catchments (III); Yenisei River catchments, right bank (IV). The major-ion chemistry of the sampled river waters records a combination of geological, geomorphological, and hydrological conditions in the four provinces. The features typical of the northern West Siberian Plain are especially prominent in province II, which has the lowest average total of major ions (Σmi), the highest chemical oxygen demand (potassium dichromate COD), and the highest contents of Fe and phosphates. The Σmi value is the highest in province IV. The river waters from four provinces share similarity in quite high organic contents (both potassium dichromate and permanganate COD), as well as high NH4+ and Fe. The long-term average Σmi of the waters is predicted not to change much in the coming one or two decades, though it may decrease slightly in the winter season but increase in the fall and spring time.

Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry

Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.

An Age Scale for the First Shallow (Sub-)Antarctic Ice Core from Young Island, Northwest Ross Sea

The climate of the sub-Antarctic is important in understanding the environmental conditions of Antarctica and the Southern Ocean. However, regional climate proxy records from this region are scarce. In this study, we present the stable water isotopes, major ion chemistry, and dust records from the first ice core from the (sub-)Antarctic Young Island. We present and discuss various dating approaches based on commonly used ice core proxies, such as stable water isotopes and seasonally deposited ions, together with site-specific characteristics such as melt layers. The dating approaches are compared with estimated precipitation rates from reanalysis data (ERA5) and volcanic cryptotephra shards likely presenting an absolute tie point from a 2001 CE eruption on neighboring Sturge Island. The resulting ice core age scale spans the period 2016 to 1995, with an uncertainty of ±2 years.

A Study on Groundwater Quality Based on Major Ion Chemistry of Jharkhand State in India: A Review

Groundwater is prime and major source of drinking water in our world. Groundwater in Jharkhand is also used for drinking, domestic, irrigation, mining and industrial etc. purposes. In Jharkhand some population are suffering from scarcity of pure drinking water and some population have partial facility with drinking water as groundwater of many area of Jharkhand are contaminated with fluoride, arsenic, heavy metals and iron etc. dangerous chemicals. This review paper focuses on current status of groundwater and contamination of different water quality parameters based on major ion chemistry in Jharkhand. The discussed water quality parameters in this study are water temperature, pH, electrical conductivity, total dissolved solid, total hardness, calcium, magnesium, iron, sodium, potassium, chloride, fluoride, arsenic, carbonate, bicarbonate, phosphate, nitrate and sulphate.