[HTML][HTML] Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework
Climate change and rapid urbanisation exacerbated multiple urban issues threatening
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
Estimating ground-level particulate matter concentrations using satellite-based data: a review
Particulate matter (PM) is a widely used indicator of air quality. Satellite-derived aerosol
products such as aerosol optical depth (AOD) have been a useful source of data for ground …
products such as aerosol optical depth (AOD) have been a useful source of data for ground …
Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US
Smoke from wildfires is a growing health risk across the US. Understanding the spatial and
temporal patterns of such exposure and its population health impacts requires separating …
temporal patterns of such exposure and its population health impacts requires separating …
Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia
Abstract In East Asia, air quality has been recognized as an important public health problem.
In particular, the surface concentrations of air pollutants are closely related to human life …
In particular, the surface concentrations of air pollutants are closely related to human life …
[HTML][HTML] Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations …
Abstract Background Spatial linear Land-Use Regression (LUR) is commonly used for long-
term modeling of air pollution in support of exposure and epidemiological assessments …
term modeling of air pollution in support of exposure and epidemiological assessments …
Dynamic assessment of PM2. 5 exposure and health risk using remote sensing and geo-spatial big data
In the past few decades, extensive epidemiological studies have focused on exploring the
adverse effects of PM 2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) …
adverse effects of PM 2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) …
Spatio-temporal modeling of PM2. 5 risk map** 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 …
[HTML][HTML] Construction of a virtual PM2. 5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient …
With increasing public concerns on air pollution in China, there is a demand for long-term
continuous PM 2.5 datasets. However, it was not until the end of 2012 that China …
continuous PM 2.5 datasets. However, it was not until the end of 2012 that China …
[HTML][HTML] Geographical and temporal encoding for improving the estimation of PM2. 5 concentrations in China using end-to-end gradient boosting
N Yang, H Shi, H Tang, X Yang - Remote Sensing of Environment, 2022 - Elsevier
Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM 2.5) profoundly
affects environmental systems and human health. To dynamically monitor fine particulate …
affects environmental systems and human health. To dynamically monitor fine particulate …
[HTML][HTML] New interpretable deep learning model to monitor real-time PM2. 5 concentrations from satellite data
Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm
(PM 2.5) is a key air quality parameter. A real-time knowledge of PM 2.5 is highly valuable …
(PM 2.5) is a key air quality parameter. A real-time knowledge of PM 2.5 is highly valuable …