TY - GEN
T1 - Hydrometeorological Drivers of Particulate Matter Using Bayesian Model Averaging
AU - Lee, Seulchan
AU - Jeong, Jaehwan
AU - Choi, Minha
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this study, we tried to find out the relationships between Particulate Matter (PM) and several hydrometeorological factors from satellite and reanalysis data, also setting a goal to figure out the way to overcome limitations that ground-based air pollution measurements have. We used particulate matter data from Air Korea, which is being managed by the Korean ministry of environment, Aerosol Optical Depth (AOD) from satellite Terra and Aqua and 9 hydrometeorological variables from Global Land Data Assimilation System (GLDAS). The relationships were produced through Bayesian Model Averaging (BMA) method. The variables affected PM10 the most, were AOD, net shortwave radiation, specific humidity and precipitation and for PM2.5, they were AOD, wind speed, net shortwave radiation and precipitation. It is expected if the accumulation and analysis of the subsequent data is progressively carried out, real-time monitoring of particulate matter with higher accuracy will be possible, totally without ground-based measurements.
AB - In this study, we tried to find out the relationships between Particulate Matter (PM) and several hydrometeorological factors from satellite and reanalysis data, also setting a goal to figure out the way to overcome limitations that ground-based air pollution measurements have. We used particulate matter data from Air Korea, which is being managed by the Korean ministry of environment, Aerosol Optical Depth (AOD) from satellite Terra and Aqua and 9 hydrometeorological variables from Global Land Data Assimilation System (GLDAS). The relationships were produced through Bayesian Model Averaging (BMA) method. The variables affected PM10 the most, were AOD, net shortwave radiation, specific humidity and precipitation and for PM2.5, they were AOD, wind speed, net shortwave radiation and precipitation. It is expected if the accumulation and analysis of the subsequent data is progressively carried out, real-time monitoring of particulate matter with higher accuracy will be possible, totally without ground-based measurements.
KW - AOD
KW - BMA
KW - Hydrometeorology
KW - PM
UR - https://www.scopus.com/pages/publications/85077693397
U2 - 10.1109/IGARSS.2019.8898203
DO - 10.1109/IGARSS.2019.8898203
M3 - Conference contribution
AN - SCOPUS:85077693397
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 7634
EP - 7637
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -