We paired existing land use regression (LUR) models for ambient ultrafine particles in Montreal and Toronto, Canada with satellite images and deep convolutional neural networks as a means of extending the spatial coverage of these models. Our finding...
Environmental pollution (Barking, Essex : 1987)
May 27, 2019
Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather R...
BACKGROUND: Accurate estimation of nitrogen dioxide (NO) and nitrogen oxide (NO) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acut...
This study presents a machine-learning-enhanced method of modeling PM personal exposures in a data-scarce, rural, solid fuel use context. Data collected during a cookstove (Africa Clean Energy (ACE)-1 solar-battery-powered stove) intervention program...
Journal of environmental sciences (China)
Nov 15, 2018
Beijing-Tianjin-Hebei area is suffering from atmospheric pollution from a long time. The understanding of the air pollution mechanism is of great importance for officials to design strategies for the environmental governance. Mixing layer height (MLH...
Environmental pollution (Barking, Essex : 1987)
Aug 11, 2018
Fine particulate matter (PM) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to m...
Environmental science and pollution research international
Jul 24, 2018
Due to the increasingly severe situation regarding adaptation to climate change, global attention has focused on whether China can fulfill its commitment to the Paris Agreement as the largest producer of carbon dioxide (CO) emissions. In this study, ...
Environmental pollution (Barking, Essex : 1987)
Jun 23, 2018
In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM...
Environmental science and pollution research international
May 24, 2018
We present a modelling concept for evaluating the impacts of anthropogenic activities suspected to be from gas flaring on the quality of the atmosphere using domestic roof-harvested rainwater (DRHRW) as indicator. We analysed seven metals (Cu, Cd, Pb...
Journal of the Air & Waste Management Association (1995)
May 24, 2018
UNLABELLED: This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of ide...
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