Record Details

Continuous daily maps of fine particulate matter (PM2.5) air quality in East Asia by application of a random forest algorithm to GOCI geostationary satellite data

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

View Archive Info
 
 
Field Value
 
Title Continuous daily maps of fine particulate matter (PM2.5) air quality in East Asia by application of a random forest algorithm to GOCI geostationary satellite data
 
Identifier https://doi.org/10.7910/DVN/0L3IP7
 
Creator Pendergrass, Drew
Daniel J. Jacob
Shixian Zhai
Jhoon Kim
Ja-Ho Koo
Seoyoung Lee
Minah Bae
Soontae Kim
Hong Liao
 
Publisher Harvard Dataverse
 
Description We use 2011-2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6km x 6km resolution over South Korea, eastern China, and Japan. We use PM2.5 observations from national networks to train and cross-validate a random forest (RF) algorithm that predicts PM2.5 from the gap-filled GOCI AOD, meteorological variables, and other predictor variables. The predicted 24-h PM2.5 for sites entirely withheld from training in a ten-fold crossvalidation procedure correlates highly with observed concentrations (R2 = 0.89) with single-value precision of 26-32% depending on country. Prediction of annual mean values has R2 = 0.96 and single-value precision of 12%. More information is available in the associated publication. Here we supply a NetCDF containing the inferred daily PM2.5 fields from 2011-19 for use in further research. If you use this data, please cite the associated publication, and feel free to reach out via email to discuss this work.
 
Subject Earth and Environmental Sciences
PM2.5
air quality
remote sensing
machine learning
particulate matter
geostationary satellite
AOD
GOCI
 
Contributor Pendergrass, Drew