AIMC Topic: Dust
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Predicting the amount of toxic metals and metalloids in silt loading using neural networks.
Environmental monitoring and assessment
Apr 1, 2025
Material deposited on road surfaces, called road dust, are known to contain different toxic elements. According to particle size, there are different fractions. Particles with an aerodynamic size less than or equal to 75 µm are called silt loading. A...
Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations.
Scientific reports
Mar 17, 2025
This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induce...
A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.
Frontiers in public health
Jan 10, 2025
Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by chronic pulmonary inflammation and progressive fibrosis, potentially leading to respiratory and/or he...
Prediction and validation of mild cognitive impairment in occupational dust exposure population based on machine learning.
Ecotoxicology and environmental safety
Sep 26, 2024
OBJECTIVE: Workers exposed to dust for extended periods may experience varying degrees of cognitive impairment. However, limited research exists on the associated risk factors. This study aims to identify key variables using machine learning algorith...
Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices.
Environmental pollution (Barking, Essex : 1987)
Jul 27, 2024
The widespread presence of Microplastics (MPs) is increasing in the indoor environment due to increasing annual plastic usage, which is becoming a global threat to human health. Therefore, this is the first research in Bangladesh to identify, and cha...
Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning.
Environmental pollution (Barking, Essex : 1987)
Apr 25, 2024
Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there r...
Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.
Journal of hazardous materials
Apr 18, 2024
Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in...
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind.
Environmental pollution (Barking, Essex : 1987)
Dec 5, 2023
Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP concentrations are necessary to mitigate their negative effe...
Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model.
Environmental science & technology
Nov 14, 2023
Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicabil...
Use of machine learning and deep learning to predict particulate Cs concentrations in a nuclearized river.
Journal of environmental radioactivity
Sep 15, 2023
Cesium-137, discharged by nuclear installations under normal operations and deposited in watersheds following atmospheric testing and accidents (i.e. Chernobyl, Fukushima …), has been studied for decades. Thus, modelling of Cs concentration in rivers...
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