Current Update on Air Pollution or Quality and Meteorological Variables: A Review and Bibliometric Analysis DOI
Merita Gidarjati, Muhammad Ma’arij Harfadli, Toru Matsumoto

et al.

ENERGY ENVIRONMENT & STORAGE, Journal Year: 2024, Volume and Issue: 4(3), P. 71 - 78

Published: Sept. 30, 2024

The study aims to investigate the existing understanding of air pollution and meteorological variables, with goal identifying assessing research patterns, areas where is lacking, variables that are important for research. Scopus Database utilized as a data source, specifically searching literature published in last 10 years using keywords "Air pollution" or quality" "Meteorological variables". utilizes VOSviewer software examine data, emphasizing noteworthy trends on climatic factors. produced map analysis expansion scholarly publication concerning above themes it identified four significant clusters. also statistical models, tools, sophisticated modeling methodologies both subjects. focuses current need attention, factors influence It offers valuable relationship between pollution, their impact public health. This enhances our comprehension complexity factors, underscoring significance data-driven analysis, methodologies, interdisciplinary approaches tackling environmental concerns.

Language: Английский

Exploration of transfer learning techniques for the prediction of PM10 DOI Creative Commons

Michael Poelzl,

Roman Kern, Simonas Kecorius

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 23, 2025

Abstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such including the prediction pollution concentrations, yet demanding extensive training data. To address this, techniques like transfer (TL) leverage knowledge from a model trained on rich dataset enhance one sparse dataset, provided there similarities in data distribution. In our experimental setup, we utilize meteorological and pollutant multiple governmental measurement stations Graz, Austria, supplemented by station Zagreb, Croatia simulate scarcity. Common ML as Random Forests, Multilayer Perceptrons, Long-Short-Term Memory, Convolutional Neural Networks explored predict particulate matter both cities. Our detailed analysis PM 10 suggests that between cities features exist further exploited. Hence, TL appears offer viable approach predictions for Zagreb station, despite challenges posed results demonstrate feasibility different improve transferring all Graz transferred Zagreb. Through investigation, discovered selectively choosing time spans based seasonal patterns only aids reducing amount needed successful but also significantly improves performance. Specifically, Forest using it with 20% labelled resulted 22% enhancement compared directly testing

Language: Английский

Citations

1
Key toxic components and sources affecting oxidative potential of atmospheric particulate matter using interpretable machine learning: Insights from fog episodes DOI
Ruiyu Li, Caiqing Yan,

Qingpeng Meng

et al.

Journal of Hazardous Materials, Journal Year: 2023, Volume and Issue: 465, P. 133175 - 133175

Published: Dec. 7, 2023

Language: Английский

Citations

20
Machine learning-based global air quality index development using remote sensing and ground-based stations DOI Creative Commons
Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti

et al.

Environmental Advances, Journal Year: 2023, Volume and Issue: 15, P. 100456 - 100456

Published: Nov. 19, 2023

Air pollution refers to the presence of hazardous substances in air that has adverse effects on health, causing millions premature deaths annually. Ground-based stations can provide accurate measurements for monitoring pollution. However, spatial coverage is limited by number measurement instruments available specific hotspot areas. Satellite remote sensing reduce uncertainty; however, results are mostly upper atmosphere with high sensitivity. To better represent surface conditions, this study aims model Quality Index pollutants CO, NO2, SO2, PM2.5, and PM10 global region using remotely sensed data. support study, 425 data points from distributed globally combined Machine Learning Linear Regression methods. Furthermore, socioeconomic environmental satellite form Multiple models. According Models more than single models, showing addition enhance accuracy. The expected help regions without estimate quality index data, turn, preventing disasters.

Language: Английский

Citations

18
Machine learning techniques to predict atmospheric black carbon in a tropical coastal environment DOI

Priyadatta Satpathy,

R. Boopathy, Mukunda M. Gogoi

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 34, P. 101154 - 101154

Published: Feb. 14, 2024

Language: Английский

Citations

6
The cellulose hydrolysis into glucose with carbon-based solid acid catalyst via machine learning, life cycle assessment and bibliometric analysis DOI

Genmao Guo,

Fangming Jin

Fuel, Journal Year: 2024, Volume and Issue: 362, P. 130891 - 130891

Published: Jan. 9, 2024

Language: Английский

Citations

5
State-of-the-Art and Recent Advances in the Abatement of Gaseous Pollutants from Waste-to-Energy DOI Creative Commons
Marco Schiavon, Marco Ravina, Mariachiara Zanetti

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(3), P. 552 - 552

Published: Jan. 23, 2024

Despite their key role in integrated waste management, direct (incineration) and indirect (gasification/pyrolysis) combustion processes are still opposed by some of the general public due to past emission levels air pollutants. In fact, although release pollutants (especially dioxin) atmosphere from has gradually decreased over years, thanks introduction stricter regulations more advanced removal technologies, there is an unsolved problem regarding acceptance waste-to-energy facilities. The aim this paper provide overview state-of-the-art pollution control (APC) technologies used Air designed reduce or eliminate emissions harmful into atmosphere. These important for safeguarding health, protecting ecosystems, complying with regulations, promoting a sustainable resilient future both local global communities. This will highlight complexity behind efforts made sector years. also propose suggested configurations based on interactions/complementarity between different APC recent findings improve performance.

Language: Английский

Citations

5
Investigating the influence of platform design on the distribution of traffic particulate matter at the bus stop DOI
Kaixuan Liu,

Xinyuan Lin,

Jiamin Xu

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 255, P. 111395 - 111395

Published: March 8, 2024

Language: Английский

Citations

5
Bibliometric Analysis on Global Research Trends in Air Pollution Prediction Research Using Machine Learning from 1991–2023 Using Scopus Database DOI
Asif Ansari, Abdur Rahman Quaff

Aerosol Science and Engineering, Journal Year: 2024, Volume and Issue: 8(3), P. 288 - 306

Published: March 28, 2024

Language: Английский

Citations

5
Research progress of machine learning in the field of photocatalysis applications DOI
Kun Li, Haoyuan Du, Lei Liu

et al.

Journal of Industrial and Engineering Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Machine learning-based white-box prediction and correlation analysis of air pollutants in proximity to industrial zones DOI
Saeed Karimi, Milad Asghari,

Reza Rabie

et al.

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 178, P. 1009 - 1025

Published: Sept. 4, 2023

Language: Английский

Citations

10