Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index

This study assessed the hydrochemical quality of groundwater in the Angads plain using the pollution index of groundwater (PIG), and microbiological indicators. Two sampling campaigns were carried out in 2023 (wet and dry seasons), with 45 samples taken each year. The analyses revealed that the majority of the ions studied exceeded the limits recommended and authorized by the World Health Organization (WHO). The PIG revealed that over 80% of samples fall into the insignificant and low pollution categories, with pollution levels increasing during the dry season. Microbiological analyses revealed the presence of fecal coliforms (FC), total coliforms (TC), intestinal enterococci (IE) and sulfate-reducing clostridium (SRC) with average levels of 52, 80, 2, and 0 CFU/100 mL during the wet season, while during the dry season these values increased to 79, 120, 3, and 0 CFU/100 mL, respectively. This study offers vital information on groundwater pollution, enabling decision makers, residents, and scientists to better identify safe water sources and areas at risk. The findings highlight the urgency of implementing effective treatment solutions and devising sustainable management plans to preserve these water resources over the long term.

Establishing robust geochemical baselines in the hyper-arid Atacama Desert remains challenging because of extreme climatic gradients, polymetallic mineralisation, and decades of intensive mining. To disentangle natural lithogeochemical signals from anthropogenic inputs, a region-wide, multi-institutional soil dataset (1404 samples; 32 elements) was compiled. The analytical workflow integrated compositional data analysis (CoDA) with isometric log-ratio transformation (ILR), principal component analysis (PCA), robust principal component analysis (RPCA), and consensus anomaly detection via hierarchical (HC) and spectral clustering (SC), applied both with and without spatial coordinates to capture compositional structure and geographic autocorrelation. Optimal cluster solutions differed among laboratory subsets (k = 2–17), reflecting instrument-specific biases. The dual workflows flagged 76 (geochemical-only) and 83 (geo-spatial) anomalies, of which 33 were jointly identified, yielding high-confidence exclusions. Regional baselines for 13 priority elements were subsequently computed, producing thresholds such as As = 66.9 mg · kg−1, Pb = 53.6 mg · kg−1, and Zn = 166.8 mg · kg−1. Incorporating spatial variables generated more coherent, lithology-aligned clusters without sacrificing sensitivity to geochemical extremes (Jaccard index = 0.26). These findings demonstrate that a reproducible, compositional-aware machine learning workflow can separate overlapping geogenic and anthropogenic signatures in heterogeneous terrains. The resulting baselines provide an operational reference for environmental monitoring in northern Chile and a transferable template for other arid mining locations.

Plateau–orogenic belts host a substantial share of global gold resources, yet quantitative prospectivity mapping is challenged by complex mineralization and strongly heterogeneous, multi-scale datasets. Using the Mayoumu area (Tibet) as a representative orogenic gold district, we develop an integrated multi-source workflow that fuses remote-sensing alteration information with regional geochemical and structural constraints within an ensemble-learning framework. Alteration anomalies were mapped from GF-5 hyperspectral imagery using mixture-tuned matched filtering (MTMF) and from Sentinel-2 multispectral imagery using the iCrosta method to extend alteration signals across scales. Geochemical anomalies were extracted from 1:200,000 stream-sediment data through isometric log-ratio (ILR) transformation and robust principal component analysis (RPCA). At the same time, ore-controlling structures were quantified using Euclidean-distance-to-fault layers. Three Boosting-based ensemble models—gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were trained to predict mineral prospectivity. Performance was evaluated using confusion matrix metrics and ROC–AUC, and key predictors were interpreted using SHAP. All three models achieved AUC values > 0.90, with LightGBM performing best (AUC = 0.94) and delineating high-prospectivity zones that coincide with known occurrences and highlight additional targets. The proposed workflow provides a practical, transferable reference for gold prospectivity mapping in complex orogenic belts worldwide.

Nickel mining operations can significantly impact groundwater quality by releasing metallic contaminants. This study aims to quantify metal concentrations in groundwater quality changes at nickel mining sites in Pomalaa District, Kolaka Regency. The study employs the Wenner configuration geoelectric technique and Pollution Index (IP) to assess metal pollution in groundwater. Geoelectric data, log borings, laboratory results of groundwater quality, and regional geological maps were used in the study. Water sample analysis detected toxic elements such as Aluminum, Iron, Manganese, Zinc, Copper, Hexavalent Chromium, Nickel, Cobalt, Cadmium, and Lead. Geoelectric analysis shows that groundwater is generally found within fracture zones of igneous and metamorphic rocks at depths between 2 and 100 meters. The Pollution Index (IP) categorized the groundwater samples as moderately contaminated, failing to meet drinking water quality standards. The analysis suggests that the groundwater originates from fissures in ultramafic rock, with significant metal contamination detected in groundwater wells. Metal content in groundwater occurs due to the interaction between rocks and groundwater. The study results also show that the groundwater pollution index is in the light category. However, the levels of heavy metals mean that groundwater cannot be directly used to fulfill needs. Further processing of groundwater is necessary so that groundwater can be used.

One of the most robust empirical findings in political science is that in multiparty democracies cabinet ministries are distributed in rough proportion to parties’ legislative seat shares, a pattern known as Gamson’s Law. Yet existing research often overlooks the fact that portfolio and seat shares are compositional—mutually dependent parts of a whole. Standard methods treat them as unconstrained, risking bias, misleading uncertainty estimates, and flawed inference. Unfortunately, the most common strategy for handling compositions—the additive log-ratio (ALR) transformation combined with seemingly unrelated regression (SUR)—fails when the number and identity of compositional elements (like parties) vary across cases. We propose the isometric log-ratio (ILR) transformation—new to political science—as an alternative that both respects compositional geometry and adapts to differing compositional structures. Monte Carlo simulations show that ILR sharply outperforms standard approaches, reducing bias, improving coverage probability, and increasing statistical power. While we apply it to portfolio allocation, ILR provides a general solution for modeling compositional outcomes with other potential uses, including in electoral competition, where ALR+SUR has required strong assumptions or ad hoc adjustments. Using this improved methodology, we find that seat–portfolio proportionality is weaker overall than conventionally reported and varies substantially across governments.

This study assessed groundwater quality in Yabelo, Elewaye, Gomole, and Duduluk towns in Ethiopia, analyzing 60 samples across 19 physicochemical parameters. The groundwater pollution index (GPI), nitrate pollution index (NPI), and water quality index (WQI) were used to evaluate drinking water suitability. Results showed turbidity, pH, bicarbonate, nitrite, and copper levels were within the World Health Organization recommended limits. However, 20% of the samples had high total dissolved solids and sulfate levels. Total hardness exceeded limits in 60% of the samples, and 40% had elevated nitrate concentrations. Chromium and fluoride were elevated by 10%, while total iron and manganese exceeded standards by 20%. The GPI indicated “Insignificant pollution” in 80% of samples and “Low pollution” in 20%. Among the samples, the NPI classified 50% as “Clean”, 10% as “Low pollution”, 30% as “Moderate”, and 10% as “Very high pollution”. The WQI rated 20% as “Good”, 30% as “Very good”, and 50% as “Excellent”. This study provides valuable insights to help authorities in identifying protective measures and treatment methods for water resources.

Groundwater quality assessment using pollution index of groundwater (PIG), ecological risk index (ERI) and hierarchical cluster analysis (HCA): A case study

A new class of [formula omitted]-transformations for the spatial analysis of Compositional Data

In order to understand the hydrogeochemistry of Mathura city, western Uttar Pradesh a total of 92, 34 and 22 groundwater samples were collected from shallow, intermediate and deep wells during post-monsoon 2017 and pre-monsoon 2018 respectively and analyzed for major cations and anions. The groundwater of the study area is found to be alkaline in nature and is categorized as hard to very hard water type. Dominant facies deciphered from Piper’s trilinear diagram of shallow, intermediate wells are Na-K-Cl-SO4 and Ca-Mg-Cl-SO4 while deep well samples are Ca-Mg-Cl-SO4 type facies in post-monsoon 2017. During pre-monsoon 2018, the ionic facies for shallow, intermediate and deep well samples is Na-K-Cl-SO4 type with the exception of 7 samples of shallow well which shows Ca-Mg-Cl-SO4 type facies. Based on the water quality index (WQI) classification, majority of samples of shallow, intermediate and deep well fall in poor water category in both seasons. According to pollution index of groundwater (PIG) classification, majority of samples of all three depths showed low to moderate pollution in both the seasons. Saturation indices of minerals was also calculated by employing geochemical program PHREEQC. Groundwater at shallow depth possesses excess concentration of Ca, Na, K, and SO4, followed by inter-mediate and deeper depth samples of both seasons. Trace elements like As, Mo, Sr, and Zn show higher concentration in shallow depth samples followed by intermediate and deeper depth samples, except As which shows a reverse trend. This demonstrates that the groundwater from shallow wells is definitely more vulnerable to contamination. The study has revealed poor groundwater quality of Mathura city and noticeable influence of urbanization and industrialization on water quality.

Population density, urbanization and industrialization are the rapidly increasing and it's causes severe damages to freshwater ecosystem. The present study aims to assess the quality of groundwater using integrated methods in the industrial zone of South India. The integrated assessment of groundwater using unsupervised machine learning method associated human health risk assessment are identified as research gap in the study region. We collected 55 samples based on groundwater availability, population density, and industrial activity in summer and winter seasons. The study found that calcium-chloride and mixed calcium-magnesium-chloride types of water are the dominating category in both seasons. The groundwater pollution index (GPI) and piper trilinear and gibbs diagram indicate that evaporation, water-rock interface, and other anthropogenic activities are the dominating factors in groundwater chemistry. Analysis using the entropy water quality index (EWQI) and human health risk estimation confirmed that nitrate is the key parameter affecting groundwater sustainability. Unsupervised machine learning techniques were applied to evaluate groundwater chemistry. The principal component analysis (PCA) results revealed that seasonal variation is influenced by mineral dissolution, rainwater recharge, and anthropogenic activities. The hierarchical cluster analysis (HCA) results shows that a tight cluster forms among pH, Na, K, NO3, and F, suggesting that processes such as agricultural inputs from fertilizer use, synthetic pesticides, and natural geochemical interactions control ion exchange. The k-means clustering yielded three clusters: low-salinity fresh water, moderately mineralized water undergoing geochemical alteration, and high-hardness water indicative of specific geogenic and anthropogenic influences in both seasons. Overall, the integrated assessment of groundwater revealed that the water-rock interface, evaporation, and synthetic fertilizer use in agricultural fields are significant factors controlling groundwater quality. The keyfindings of this study provides clear knowledge about the nature of groundwater, influencing factors and help to improve the water resources management strategies in investigation zone.

Meal timing is suggested to influence the obesity risk in children. Our aim was to analyse the effect of energy and nutrient distributions at eating occasions (EO), including breakfast, lunch, supper, and snacks, on the BMI z-score (zBMI) during childhood in 729 healthy children. BMI and three-day dietary protocols were obtained at 3, 4, 5, 6, and 8 years of age, and dietary data were analysed as the percentage of the mean total energy intake (TEI; %E). Intakes at EOs were transformed via an isometric log–ratio transformation and added as exposure variables to linear mixed-effects models. Stratified analyses by country and recategorization of EOs by adding intake from snacks to respective meals for further analyses were performed. The exclusion of subjects with less than three observations and the exclusion of subjects who skipped one EO or consumed 5% energy or less at one EO were examined in sensitivity analyses. Around 23% of the children were overweight at a given time point. Overweight and normal-weight children showed different distributions of dietary intakes over the day; overweight children consumed higher intakes at lunch and lower intakes of snacks. However, no significant effects of timing of EOs on zBMI were found in regression analyses.

An integrated appraisal of the hydrogeochemistry and the potential public health risks of groundwater nitrate and fluoride in eastern Ghana

As an important source of domestic water and drinking water, the groundwater quality is crucial. Reasonable screening of typical groundwater pollutants is crucial for studying groundwater quality. At present, most research in Linyi focus on the distribution and utilization of water resources, few research focuses on groundwater quality. Therefore, this paper introduces a method of screening priority pollutants, that is, the screening method based on pollution assessment. This method can help to clarify research priorities and make targeted recommendations. In this paper, the study area is a Development Zone of Linyi. Based on groundwater monitoring data of November 2012 and November 2017, a screening method of groundwater priority control pollutants based on pollution assessment is applied to screen out typical pollutants of shallow groundwater quality. It will provide reference for future managements and planning of groundwater in the Development Zone. After pollutants screening and assignment calculation, the results show that the typical pollutants in groundwater are nitrite, anionic surfactant, ammonia nitrogen, fluoride, sodium, nitrate, permanganate index, sulfate, chloride, total hardness, total dissolved solids.

Review for "Appraising groundwater quality and probabilistic human health risks from fluoride-enriched groundwater using the pollution index of groundwater (PIG) and GIS: a case study of adama town and its vicinities in the central main Ethiopian rift valley"

Decision letter for "Appraising groundwater quality and probabilistic human health risks from fluoride-enriched groundwater using the pollution index of groundwater (PIG) and GIS: a case study of adama town and its vicinities in the central main Ethiopian rift valley"