AIMC Topic: Water Pollutants

Clear Filters Showing 1 to 10 of 16 articles

Groundwater quality and risk in the Ganga River Basin: an integrated machine learning appraisal.

Environmental geochemistry and health
Groundwater supports the livelyhoods of hundreds of millions across the Ganga River Basin (GRB), yet its quality is increasingly stressed by geogenic and anthropogenic factors. Using a high-density 2022-dataset from 3417 wells, this study integrates ...

A methodology for coagulant virtual testing to improve dissolved organic matter removal in surface water treatment.

The Science of the total environment
Coagulation is one of the most crucial steps in a Drinking Water Treatment Plant (DWTP). The coagulant dose required for the removal of particles and natural organic matter (NOM) is typically determined through jar tests. However, this method is time...

Robotic-assisted dynamic large drop microextraction.

Journal of chromatography. A
By proper design of an innovative extraction device, a lab-made multipurpose autosampler was exploited in the automated performance of the dynamic large drops based microextraction. The pluses of this new analytical strategy were demonstrated in the ...

Combination of LEDs and cognitive modeling to quantify sheep cheese whey in watercourses.

Talanta
The concentration of sheep cheese whey (CW) in water obtained from two Spanish reservoirs, two Spanish rivers, and distilled water has been estimated by combining spectroscopic measurements, obtained with light-emitting diodes (LEDs), and linear or n...

Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

Environmental monitoring and assessment
Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a fe...

Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model.

International journal of environmental research and public health
Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for e...

Evaluation of the bias and precision of regression techniques and machine learning approaches in total dissolved solids modeling of an urban aquifer.

Environmental science and pollution research international
TDS is modeled for an aquifer near an unlined landfill in Canada. Canadian Drinking Water Guidelines and other indices are used to evaluate TDS concentrations in 27 monitoring wells surrounding the landfill. This study aims to predict TDS concentrati...

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence.

Chemosphere
Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major t...

In vitro effects of virgin microplastics on fish head-kidney leucocyte activities.

Environmental pollution (Barking, Essex : 1987)
Microplastics are well-documented pollutants in the marine environment that result from production or fragmentation of larger plastic items. The knowledge about the direct effects of microplastics on immunity, including fish, is still very limited. W...

Metal-free virucidal effects induced by g-CN under visible light irradiation: Statistical analysis and parameter optimization.

Chemosphere
Waterborne viruses with a low infectious dose and a high pathogenic potential pose a serious risk for humans all over the world, calling for a cost-effective and environmentally-friendly inactivation method. Optimizing operational parameters during t...