Environmental monitoring and assessment
Jun 28, 2019
PM air pollution is a significant issue for human health all over the world, especially in East Asia. A large number of ground-based measurement sites have been established over the last decade to monitor real-time PM concentration. However, even thi...
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms hav...
International journal of environmental research and public health
Jun 17, 2019
Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observ...
The main aim of this work is to study gas-phase toluene removal in one- and two-liquid phase biotrickling filters (O/TLP-BTF) and model the BTF performance using artificial neural networks (ANNs). The TLP-BTF was operated for 60 d in the presence of ...
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...
Computational intelligence and neuroscience
May 23, 2019
Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precauti...
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...
International journal of environmental health research
Mar 31, 2019
This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk facto...