TY - GEN
T1 - Using machine learning approach to evaluate the PM2.5 Concentrations in China from 1998 to 2016
AU - Lin, Li
AU - Di, Liping
AU - Yang, Ruixin
AU - Zhang, Chen
AU - Yu, Eugene
AU - Rahman, Md Shahinoor
AU - Sun, Ziheng
AU - Tang, Junmei
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/27
Y1 - 2018/9/27
N2 - Pollution is one of the main negative outcomes for rapid economic growth without sustainable development in China. Different types of pollutions are harming people's health and the impacts of pollution on environment and people's health could last for decades. Fine particulate matter(PM2.5), which is one of most common types of air pollutions in China, could penetrate and sediment in human's respiratory system and cause different kind of respiratory diseases. Research has shown the strong association between Aerosol Optical Depth (AOD) and PM2.5. For this reason, remote sensing imagery could be used to estimate the level of PM2.5 concentration near ground. With utilizing PM2.5 dataset estimated by Socioeconomic Data and Applications Center (SEDAC) and machine learning approach, this paper is aimed to provide spatiotemporal comparison of PM2.5 concentrations in China. Result from this analysis could help people to better understand the recent history and current status of PM2.5 pollution in China.
AB - Pollution is one of the main negative outcomes for rapid economic growth without sustainable development in China. Different types of pollutions are harming people's health and the impacts of pollution on environment and people's health could last for decades. Fine particulate matter(PM2.5), which is one of most common types of air pollutions in China, could penetrate and sediment in human's respiratory system and cause different kind of respiratory diseases. Research has shown the strong association between Aerosol Optical Depth (AOD) and PM2.5. For this reason, remote sensing imagery could be used to estimate the level of PM2.5 concentration near ground. With utilizing PM2.5 dataset estimated by Socioeconomic Data and Applications Center (SEDAC) and machine learning approach, this paper is aimed to provide spatiotemporal comparison of PM2.5 concentrations in China. Result from this analysis could help people to better understand the recent history and current status of PM2.5 pollution in China.
KW - Air Quality
KW - MODIS
KW - PM2.5
KW - Remote Sensing
UR - https://www.scopus.com/pages/publications/85055861028
UR - https://www.scopus.com/pages/publications/85055861028#tab=citedBy
U2 - 10.1109/Agro-Geoinformatics.2018.8475987
DO - 10.1109/Agro-Geoinformatics.2018.8475987
M3 - Conference contribution
AN - SCOPUS:85055861028
T3 - 2018 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018
BT - 2018 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018
Y2 - 6 August 2018 through 9 August 2018
ER -