[HTML][HTML] An ensemble-based model of PM2. 5 concentration across the contiguous United States with high spatiotemporal resolution
Various approaches have been proposed to model PM 2.5 in the recent decade, with
satellite-derived aerosol optical depth, land-use variables, chemical transport model …
satellite-derived aerosol optical depth, land-use variables, chemical transport model …
Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States
A number of models have been developed to estimate PM2. 5 exposure, including satellite-
based aerosol optical depth (AOD) models, land-use regression, or chemical transport …
based aerosol optical depth (AOD) models, land-use regression, or chemical transport …
An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data
The long satellite aerosol data record enables assessments of historical PM2. 5 level in
regions where routine PM2. 5 monitoring began only recently. However, most previous …
regions where routine PM2. 5 monitoring began only recently. However, most previous …
Estimating daily high-resolution PM2. 5 concentrations over Texas: Machine Learning approach
PM 2.5 is an important atmospheric constituent associated to human health. Therefore, the
capability of estimating PM 2.5 concentrations at high spatiotemporal resolutions …
capability of estimating PM 2.5 concentrations at high spatiotemporal resolutions …
Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach
To estimate PM2. 5 concentrations, many parametric regression models have been
developed, while nonparametric machine learning algorithms are used less often and …
developed, while nonparametric machine learning algorithms are used less often and …
Optimizing modeling windows to better capture the long-term variation of PM2. 5 concentrations in China during 2005–2019
Including data of different time intervals during model development influences the predicting
accuracy of PM 2.5 but has not been widely discussed. Therefore, we included modeling …
accuracy of PM 2.5 but has not been widely discussed. Therefore, we included modeling …
Spatiotemporal prediction of continuous daily PM2. 5 concentrations across China using a spatially explicit machine learning algorithm
A high degree of uncertainty associated with the emission inventory for China tends to
degrade the performance of chemical transport models in predicting PM 2.5 concentrations …
degrade the performance of chemical transport models in predicting PM 2.5 concentrations …
[HTML][HTML] Ensemble-based deep learning for estimating PM2. 5 over California with multisource big data including wildfire smoke
L Li, M Girguis, F Lurmann, N Pavlovic… - Environment …, 2020 - Elsevier
Introduction Estimating PM 2.5 concentrations and their prediction uncertainties at a high
spatiotemporal resolution is important for air pollution health effect studies. This is …
spatiotemporal resolution is important for air pollution health effect studies. This is …
A new hybrid spatio-temporal model for estimating daily multi-year PM2. 5 concentrations across northeastern USA using high resolution aerosol optical depth data
The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter
(PM 2.5) for epidemiology studies has increased substantially over the past few years …
(PM 2.5) for epidemiology studies has increased substantially over the past few years …
Predicting monthly high-resolution PM2. 5 concentrations with random forest model in the North China Plain
Exposure to fine particulate matter (PM 2.5) remains a worldwide public health issue.
However, epidemiological studies on the chronic health impacts of PM 2.5 in the developing …
However, epidemiological studies on the chronic health impacts of PM 2.5 in the developing …