A review on magnetic sensors for monitoring of hazardous pollutants in water resources

A Hojjati-Najafabadi, M Mansoorianfar, T Liang… - Science of The Total …, 2022 - Elsevier
Water resources have long been of interest to humans and have become a serious issue in
all aspects of human life. The disposal of hazardous pollutants in water resources is one of …

Magnetic-MXene-based nanocomposites for water and wastewater treatment: A review

A Hojjati-Najafabadi, M Mansoorianfar, T Liang… - Journal of Water …, 2022 - Elsevier
An increase in the pollutants such as hazardous refractory contaminations, organic dyes,
pharmaceutical and pesticide contaminants which are widely disposed to water resources …

A hybrid model for spatiotemporal forecasting of PM2. 5 based on graph convolutional neural network and long short-term memory

Y Qi, Q Li, H Karimian, D Liu - Science of the Total Environment, 2019 - Elsevier
Increasing availability of data related to air quality from ground monitoring stations has
provided the chance for data mining researchers to propose sophisticated models for …

Spatio-temporal variation of ozone pollution risk and its influencing factors in China based on Geodetector and Geospatial models

Y Chen, H Li, H Karimian, M Li, Q Fan, Z Xu - Chemosphere, 2022 - Elsevier
Ozone (O 3) has become the primary pollutant in many cities, and high concentrations of O 3
cause significant harm to the ecological environment and human health. This study …

Recent advancements in MXene-based nanocomposites as photocatalysts for hazardous pollutant degradation-A review

C Chinnasamy, N Perumal, A Choubey… - Environmental …, 2023 - Elsevier
The recent expeditious industrialization and urbanization showcase the increasing need for
renewable and non-renewable energy and the severe environmental crisis. In this regard …

Evaluation of different machine learning approaches and aerosol optical depth in PM2. 5 prediction

H Karimian, Y Li, Y Chen, Z Wang - Environmental research, 2023 - Elsevier
Abstract Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites,
has shown potential in PM 2.5 predictions. However, this important source of data suffers …

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

F Mohammadi, H Teiri, Y Hajizadeh… - Scientific Reports, 2024 - nature.com
With increasing levels of air pollution, air quality prediction has attracted more attention.
Mathematical models are being developed by researchers to achieve precise predictions …

A new hybrid deep neural network for multiple sites PM2. 5 forecasting

M Teng, S Li, J Yang, J Chen, C Fan, Y Ding - Journal of Cleaner …, 2024 - Elsevier
Many studies have confirmed that fine particulate matter (PM 2.5) poses significant hazards
to both human health and the ecological environment. Predicting future trends in PM 2.5 …

DESA: a novel hybrid decomposing-ensemble and spatiotemporal attention model for PM2.5 forecasting

S Fang, Q Li, H Karimian, H Liu, Y Mo - Environmental Science and …, 2022 - Springer
Exposure to fine particulate matter can easily lead to health issues. PM2. 5 concentrations
are associated with various spatiotemporal factors, which makes the prediction of PM2. 5 …

[HTML][HTML] Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia

SD Latif, V Lai, FH Hahzaman, AN Ahmed… - Results in …, 2024 - Elsevier
Abstract At Klang Valley, ground-level ozone is a significant source of air pollution. Ozone (O
3) concentration is affected by meteorological conditions and air pollutants. Linear …