TY - JOUR
T1 - Evaluation of using satellite-derived aerosol optical depth in land use regression models for fine particulate matter and its elemental composition
AU - Huang, Chun Sheng
AU - Liao, Ho Tang
AU - Lin, Tang Huang
AU - Chang, Jung Chi
AU - Lee, Chien Lin
AU - Yip, Eric Cheuk Wai
AU - Wu, Yee Lin
AU - Wu, Chang Fu
N1 - Funding Information:
This study was funded by National Taiwan University (105R7812) and the ‘Innovation and Policy Center for Population Health and Sustainable Environment (Population Health Research Center, PHRC), College of Public Health, National Taiwan University’ from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education of Taiwan (NTU-107L9003).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/8
Y1 - 2021/8
N2 - This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5 ) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.
AB - This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5 ) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.
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U2 - 10.3390/atmos12081018
DO - 10.3390/atmos12081018
M3 - Article
AN - SCOPUS:85112766708
SN - 2073-4433
VL - 12
JO - Atmosphere
JF - Atmosphere
IS - 8
M1 - 1018
ER -