Hong-Wei Wang
Biographic Data
| ID | 10439321 |
|---|---|
| NAME | Hong-Wei Wang |
| GIVEN NAMES | Hong-Wei |
| FAMILY NAME | Wang |
| SIGNATURE | WANG H |
| AFFILIATIONS | Shanghai Jiao Tong University |
| ORCID | 0000-0001-9393-2701 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Regional Prediction of Ozone and Fine Particulate Matter Using Diffusion Convolutional Recurrent Neural Network
Accurate air quality forecasts can provide data-driven supports for governmental departments to control air pollution and further protect the health of residents. However, existing air quality forecasting models mainly focus on site-specific time series forecasts at a local level, and rarely consider the spatiotemporal relationships among regional monitoring stations. As a novelty, we construct a diffusion convolutional recurrent neural network (…
Air Pollution and Workplace Choice
Understanding the impacts of air pollution on public health and individual behavior is crucial for optimal environmental policy design. Using 2015 census microdata in China, this paper examined the causal effect of air pollution on working place choice. The research design relies on a regression discontinuity design based on China's Huai River Policy. The discontinuity in air pollution caused by the Huai River Policy provides a natural experiment…
Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model
The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident's outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In or…
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Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model
The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident's outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In or…
Regional Prediction of Ozone and Fine Particulate Matter Using Diffusion Convolutional Recurrent Neural Network
Accurate air quality forecasts can provide data-driven supports for governmental departments to control air pollution and further protect the health of residents. However, existing air quality forecasting models mainly focus on site-specific time series forecasts at a local level, and rarely consider the spatiotemporal relationships among regional monitoring stations. As a novelty, we construct a diffusion convolutional recurrent neural network (…
Air Pollution and Workplace Choice
Understanding the impacts of air pollution on public health and individual behavior is crucial for optimal environmental policy design. Using 2015 census microdata in China, this paper examined the causal effect of air pollution on working place choice. The research design relies on a regression discontinuity design based on China's Huai River Policy. The discontinuity in air pollution caused by the Huai River Policy provides a natural experiment…
Air pollution (3 works) · Air Quality and Health Impacts (3 works) · Air quality index (3 works) · Environmental Science (3 works) · Geography (3 works) · Meteorology (3 works) · Air Quality Monitoring and Forecasting (2 works) · Computer Science (2 works) · Machine learning (2 works) · Particulates (2 works)