Arsenic removal technologies and future trends: A mini review
In addition, it also provides a comprehensive discussion of how arsenic impurities can be removed from the environment using diverse established and …
Machine Learning Models of Groundwater Arsenic Spatial …
Recent advances in machine learning methods offer the opportunity to improve risk assessment and to decipher factors influencing the spatial variability of groundwater arsenic ([As]gw). A systematic comparison reveals that boosted regression trees (BRT) and random forest (RF) outperform logistic regression. The probability of [As]gw exceeding 5 μg/L …
Machine learning approaches for predicting arsenic …
Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks. Jafar Abdi & Golshan Mazloom. Scientific …
Global threat of arsenic in groundwater | Science
Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured …
Developing robust arsenic awareness prediction models using machine
Comparing machine learning algorithms for arsenic awareness prediction. ... Arsenic awareness differed (p < .000) between those who reported that people sought their advice and valued their views (47%) and those who did not (16%). Within response groups, 73% of respondents who did not so describe themselves and 47% of those who did were …
Groundwater Arsenic Distribution in India by Machine …
1. Introduction. Around the world, but particularly so in India, there is an ever-increasing dependence on groundwater for drinking water supplies and irrigation [ 1, 2 ]. …
A simple, low-cost filter that removes arsenic and iron …
The technology removes arsenic and iron together from contaminated water at a cost less than one rupee (Re. 0.5) per 100 litres (Rs. 1 for 200 litres). Groundwater overuse has been linked to an upsurge in arsenic levels, but there have not been enough surveys in Assam to fully understand the scale of the problem.
Human-induced arsenic pollution modeling in surface …
In recent years, assessment of sediment contamination by heavy metals, i.e., arsenic, has attracted the interest of scientists worldwide. The present study provides a new methodology to better understand the factors influencing surface water vulnerability to arsenic pollution by two advanced machine learning algorithms including boosted …
Detecting Arsenic Contamination Using Satellite Imagery and Machine
This study attempts to determine if a relationship exists between soil's hyperspectral data and arsenic concentration using NASA's Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in ...
Assessment of groundwater arsenic contamination using machine …
This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern. In this study, various tree- …
Assessment of groundwater arsenic contamination using machine …
In recent years, several machine learning models have been applied to assess the quality of arsenic in groundwater, of which logistic regression is the most common model. The logistic regression model was used by Dummer et al. (2015 ) for predicting the spatial distribution of arsenic worldwide.
Groundwater Arsenic Contamination Throughout China | Science
Abstract. Arsenic-contaminated groundwater used for drinking in China is a health threat that was first recognized in the 1960s. However, because of the sheer size of the country, millions of groundwater wells remain to be tested in order to determine the magnitude of the problem. We developed a statistical risk model that classifies safe and ...
Machine-Learning Predictions of High Arsenic and High …
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict ...
Machine Learning Models of Groundwater Arsenic Spatial …
We created a global prediction map of groundwater arsenic exceeding 10μg per L using a random forest machine-learning model based on 11 geospatial environmental …
Machine-Learning Predictions of High Arsenic and High …
This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern.
Machine Learning Models of Arsenic in Private Wells …
Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). ... Machine learning models using boosted regression trees (BRT) and random forest …
Machine Learning Models of Arsenic in Private Wells …
of arsenic in the concentration ranges of ≤5 μg/L, >5 to ≤10 μg/L, and >10 μg/L. Wells were coded based on the measured arsenic concentration category, with category 1 (C1) the lowest and category 3 (C3) the highest concentration. Preliminary RFC models, including one with 4 categories and a 1 μg/L boundary, were tested but not pursued ...
(PDF) Applying Machine Learning to Arsenic Species and …
Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cas es.pdf Content available from CC BY-NC 4.0:
Predicting sustainable arsenic mitigation using machine …
The authors discovered that arsenic awareness is a nonlinear classification problem, and the SVM and RF appeared to be the machine learning algorithms that most accurately classified arsenic awareness (Singh et al., 2018). The authors further suggested that survey-based complex environmental data may require advanced computational …
Arsenic Treatment Technologies for Soil, Waste, and Water
Technologies for water include: precipitation-coprecipitation, membrane filtration, adsorption, ion exchange, permeable reactive barriers, and biological treatment. Two technologies discussed in the report address soils, other solids, and water: electrokinetics and phytoremediation. Arsenic Treatment Technologies for Soil, …
Predicting sustainable arsenic mitigation using machine …
This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve …
Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine
Machine learning approaches (e.g., random forest) can and have been used to predict the distribution of arsenic contamination in groundwater effectively [20,23,24,26,32]. To date, most of the machine learning prediction models for the distribution of arsenic in groundwater have been pure machine learning models.
Machine Learning Models of Arsenic in Private …
Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were …
[PDF] Assessment of groundwater arsenic contamination using machine
The Optimized Forest model can be used to test new water samples for classification of arsenic in groundwater samples and outperforms other classifier as it has a high accuracy, a precision, recall, and a low FPR. This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater …
Machine-Learning Predictions of High Arsenic and High
We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer …
Arsenic removal technologies and future trends: A mini …
1. Overview. Arsenic is the 20th highest natural metalloid found in the earth's crust (atomic number 33), and it is commonly recognized for adversity on human and marine animals (Yin et al., 2017).Arsenic has a (74.9 g·mol–1)atomic weight, (5.73 g·cm–3) specific gravity, boiling and melting point of 614 °C and 817 °C respectively and it occurs …
Do Brita Filters Remove Arsenic? Lead? Complete 2024 …
The study applied several concentrations of arsenic, and Brita did remove arsenic in each trial. As the arsenic concentration rose, the Brita filter's arsenic reduction rate lowered. At its peak, Brita reduced arsenic by 27.6% and at its lowest, it reduced arsenic by 19.2%. Meanwhile, the ZeroWater pitcher smoked the competition.
IIT Madras Prof T Pradeep's AMRIT filters saving lives from arsenic …
A 2021 study by IIT Kharagpur used artificial intelligence (AI)-based prediction modelling to gauge the extent of arsenic contamination in India. The study revealed that almost 20% of India's total land area has toxic levels of arsenic in its groundwater. This exposes more than 250 million people across the country to the poisonous element.
Predicting geogenic groundwater arsenic contamination risk …
1. Introduction. The distribution of high arsenic (As) in groundwater is a global concern studied for several years. The As in groundwater is known to be triggered by the result of geogenic factors (Guo et al., 2014; Podgorski and Berg, 2020; Wang et al., 2019).Therefore, the prediction of the distribution of As on a regional and global scale is …
(PDF) Predicting sustainable arsenic mitigation using machine …
Study area robust machine learning model for arsenic mitigation remains cause for debate. Bihar, the study area, is the second-worst arsenic-affected Indian Several machine learning (ML) techniques have been used in state after West Bengal, which shares its borders with other arsenic developing prediction models on environmental data, including ...
Hydrogeochemical and sediment parameters improve
To figure this out, we established a random forest machine-learning model to predict groundwater arsenic distributions in the Hetao Basin, China, by using 22 variables of climate, topographic features, soil properties, sediment characteristics, groundwater geochemicals, and hydraulic gradients of 492 groundwater samples.
A Machine Learning Approach for Spatial Mapping of the …
Groundwater resources are abundant and widely used in Taiwan's Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization's standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable sp …
How to Practice Safety in Precision Machine Shops
Therefore, any machine part that could cause injury must be safeguarded. Metal fab requires the use of hazardous substances and can also produce them. Owners who want to promote safety in machine shops must monitor for dangerous levels of toxins like lead, asbestos, inorganic arsenic, cadmium, and formaldehyde – all common OSHA citations.
Arsenic removal performance and mechanism from water on …
Arsenic (As) is a naturally ubiquitous carcinogenic metalloid in the environment, including the atmosphere, sediments, soil, minerals, groundwater, and food 1.It primarily occurs in the forms of ...
Changes of groundwater arsenic risk in different
Changes of groundwater arsenic risk in different seasons in Hetao Basin based on machine learning model. Author links open overlay panel Yu Fu a b, Wengeng Cao a b d 1, Deng Pan c, ... Groundwater arsenic distribution inIndia by machine learning geospatial modeling. Int. J. Env. Res. Public Health, 17 (2020), pp. 7119-7135, 10.3390 ...
The case of arsenic contamination in the Sardinian Geopark, …
A full chemical analysis of water/soil samples were collected at the site in 2004. The data show the presence of several toxic contaminants above the national legal threshold. A symbolic machine learning classifier is employed to learn strong patterns associated with a high level of arsenic (As) in the soil samples.
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