Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
Abstract The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the...
Enregistré dans:
Auteurs principaux: | Zheng Cao, Guanhua Guo, Zhifeng Wu, Shaoying Li, Hui Sun, Wenchuan Guan |
---|---|
Format: | article |
Langue: | EN |
Publié: |
American Geophysical Union (AGU)
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/4a8a8cf817034b978e3fdebbd3d0e0e2 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Estimating Intra‐Urban Inequities in PM2.5‐Attributable Health Impacts: A Case Study for Washington, DC
par: Maria D. Castillo, et autres
Publié: (2021) -
Resilience: Directions for an Uncertain Future Following the COVID‐19 Pandemic
par: Stephanie Galaitsi, et autres
Publié: (2021) -
Testing Homes for Potential Sources of Lead Exposure as a High‐School Science Project
par: Evan M. Sefchick, et autres
Publié: (2021) -
Integrating hazard, exposure, vulnerability and resilience for risk and emergency management in a volcanic context: the ADVISE model
par: Costanza Bonadonna, et autres
Publié: (2021) -
Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
par: Zhao Xiao Liang, et autres
Publié: (2021)