[HTML][HTML] A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective …

X Ma, B Zou, J Deng, J Gao, I Longley, S ** and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China
J Wei, Z Li, K Li, RR Dickerson, RT Pinker… - Remote Sensing of …, 2022 - Elsevier
Ozone (O 3) is an important trace and greenhouse gas in the atmosphere, posing a threat to
the ecological environment and human health at the ground level. Large-scale and long …

[HTML][HTML] Application of land use regression model to assess outdoor air pollution exposure: A review

WNFW Azmi, TR Pillai, MT Latif, S Koshy… - Environmental …, 2023 - Elsevier
In this study, we reviewed the application of land use regression (LUR) models in various
regions worldwide to provide insight into approaches utilized for LUR models. We also …

Estimating spatiotemporal variation in ambient ozone exposure during 2013–2017 using a data-fusion model

T Xue, Y Zheng, G Geng, Q **ao, X Meng… - … science & technology, 2020 - ACS Publications
Since 2013, clean-air actions in China have reduced ambient concentrations of PM2. 5.
However, recent studies suggest that ground surface O3 concentrations increased over the …

Using a land use regression model with machine learning to estimate ground level PM2. 5

PY Wong, HY Lee, YC Chen, YT Zeng, YR Chern… - Environmental …, 2021 - Elsevier
Ambient fine particulate matter (PM 2.5) has been ranked as the sixth leading risk factor
globally for death and disability. Modelling methods based on having access to a limited …

Fine particulate matter (PM2. 5) trends from land surface changes and air pollution policies in China during 1980–2020

R Yousefi, A Shaheen, F Wang, Q Ge, R Wu… - Journal of environmental …, 2023 - Elsevier
High levels of fine particulate matter (PM 2.5) pose a severe air pollution challenge in China.
Both land use changes and anthropogenic emissions can affect PM2. 5 concentrations. Only …

[HTML][HTML] Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013–2019

X Meng, W Wang, S Shi, S Zhu, P Wang, R Chen… - Environmental …, 2022 - Elsevier
Evaluating ozone levels at high resolutions and accuracy is crucial for understanding the
spatiotemporal characteristics of ozone distribution and assessing ozone exposure levels in …

Evaluation of different machine learning approaches to forecasting PM2. 5 mass concentrations

H Karimian, Q Li, C Wu, Y Qi, Y Mo, G Chen… - Aerosol and Air Quality …, 2019 - Springer
With the rapid growth in the availability of data and computational technologies, multiple
machine learning frameworks have been proposed for forecasting air pollution. However …

Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach

A Hasnain, Y Sheng, MZ Hashmi, UA Bhatti, Z Ahmed… - Chemosphere, 2023 - Elsevier
Abstract The novel coronavirus (COVID-19), first identified at the end of December 2019, has
significant impacts on all aspects of human society. In this study, we aimed to assess the …

Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid …

S Jain, AA Presto, N Zimmerman - Environmental Science & …, 2021 - ACS Publications
Previous studies have characterized spatial patterns of pollution with land use regression
(LUR) models from distributed passive or filter samplers at low temporal resolution. Large …