Machine learning in environmental research: common pitfalls and best practices
Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …
sets and decipher complex relationships between system variables. However, due to the …
[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 Xiao… - Environment …, 2024 - Elsevier
Land use regression (LUR) models are widely used in epidemiological and environmental
studies to estimate humans' exposure to air pollution within urban areas. However, the early …
studies to estimate humans' exposure to air pollution within urban areas. However, the early …
Data-driven machine learning in environmental pollution: gains and problems
The complexity and dynamics of the environment make it extremely difficult to directly predict
and trace the temporal and spatial changes in pollution. In the past decade, the …
and trace the temporal and spatial changes in pollution. In the past decade, the …
Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion
Air pollution has altered the Earth's radiation balance, disturbed the ecosystem, and
increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air …
increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air …
[HTML][HTML] Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees
Fine particulate matter with aerodynamic diameters≤ 2.5 µ m (PM 2.5) has adverse effects
on human health and the atmospheric environment. The estimation of surface PM 2.5 …
on human health and the atmospheric environment. The estimation of surface PM 2.5 …
Estimating 1-km-resolution PM2. 5 concentrations across China using the space-time random forest approach
Abstract Fine particulate matter (PM 2.5) is closely related to the atmospheric environment
and human life. Satellite-based aerosol optical depth (AOD) products have been widely …
and human life. Satellite-based aerosol optical depth (AOD) products have been widely …
[HTML][HTML] A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen …
J Chen, K de Hoogh, J Gulliver, B Hoffmann… - Environment …, 2019 - Elsevier
Empirical spatial air pollution models have been applied extensively to assess exposure in
epidemiological studies with increasingly sophisticated and complex statistical algorithms …
epidemiological studies with increasingly sophisticated and complex statistical algorithms …
Evaluation of gap-filling approaches in satellite-based daily PM2. 5 prediction models
Approximately half of satellite aerosol retrievals are missing that limits the application of
satellite data in PM 2.5 pollution monitoring. To obtain spatiotemporally continuous PM 2.5 …
satellite data in PM 2.5 pollution monitoring. To obtain spatiotemporally continuous PM 2.5 …
A data-driven method of traffic emissions mapping with land use random forest models
The development of intelligent approaches to quantify and mitigate on-road emissions is
essential for urban and transportation sustainability for global megacities. Here, we utilize …
essential for urban and transportation sustainability for global megacities. Here, we utilize …
Spatio-temporal modeling of PM2. 5 risk mapping using three machine learning algorithms
Urban air pollution is one of the most critical issues that affect the environment, community
health, economy, and management of urban areas. From a public health perspective, PM …
health, economy, and management of urban areas. From a public health perspective, PM …