Interpretable machine learning approaches for forecasting and predicting air pollution: a systematic review

A Houdou, I El Badisy, K Khomsi, SA Abdala… - Aerosol and Air Quality …, 2024 - Springer
Many studies use machine learning to predict atmospheric pollutant levels, prioritizing
accuracy over interpretability. This systematic review will focus on reviewing studies that …

State-of-art in modelling particulate matter (PM) concentration: a sco** review of aims and methods

L Gianquintieri, D Oxoli, EG Caiani… - Environment …, 2024 - Springer
Air pollution is the one of the most significant environmental risks to health worldwide. An
accurate assessment of population exposure would require a continuous distribution of …

Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data

B Chen, Y Wang, J Huang, L Zhao, R Chen… - Science of The Total …, 2023 - Elsevier
Ozone (O 3) is an important greenhouse gas in the atmosphere. Stratospheric ozone
protects human beings, but high near-surface ozone concentrations threaten environment …

Exploring high-resolution near-surface CO concentrations based on Himawari-8 top-of-atmosphere radiation data: Assessing the distribution of city-level CO hotspots …

B Chen, J Hu, Z Song, X Zhou, L Zhao, Y Wang… - Atmospheric …, 2023 - Elsevier
Carbon monoxide (CO) has notable effects on the atmospheric environment and human
health. This study used Himawari-8 top-of-atmosphere radiation (TOAR) data …

Revealing the Covariation of Atmospheric O2 and Pollutants in an Industrial Metropolis by Explainable Machine Learning

X Liu, L Wang, J Huang, Y Wang, C Li… - … & Technology Letters, 2023 - ACS Publications
In urban areas, atmospheric O2 actively participates in the process of anthropogenic
emissions and energy consumption. However, the covariation between atmospheric O2 and …

Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China's …

B Chen, Z Song, J Huang, P Zhang… - Journal of …, 2022 - Wiley Online Library
The rapid urbanization in China and the long‐range transport dust (LRTD) from arid and
semi‐arid areas has resulted in an increase of PM10 concentration. In this study, an …

Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China

L Lin, Y Liang, L Liu, Y Zhang, D **e, F Yin, T Ashraf - Remote Sensing, 2022 - mdpi.com
Fine particulate matter (PM2. 5) is a major pollutant in Guanzhong Urban Agglomeration
(GUA) during the winter, and GUA is one of China's regions with the highest concentrations …

Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021

H Dai, G Huang, J Wang, H Zeng, F Zhou - International Journal of …, 2022 - mdpi.com
Fine particulate matter (PM2. 5) has a continuing impact on the environment, climate change
and human health. In order to improve the accuracy of PM2. 5 estimation and obtain a …

[HTML][HTML] Providing fine temporal and spatial resolution analyses of airborne particulate matter utilizing complimentary in situ IoT sensor network and remote sensing …

PMH Dewage, LOH Wijeratne, X Yu, M Iqbal… - Remote Sensing, 2024 - mdpi.com
This study aims to provide analyses of the levels of airborne particulate matter (PM) using a
two-pronged approach that combines data from in situ Internet of Things (IoT) sensor …

Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020

Q He, T Ye, W Wang, M Luo, Y Song… - Journal of Environmental …, 2023 - Elsevier
Monitoring long-term variations in fine particulate matter (PM 2.5) is essential for
environmental management and epidemiological studies. While satellite-based …