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
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. 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 a' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["air quality","AOD","geostationary satellite","GOCI","machine learning","particulate matter","PM2.5","remote sensing"]} <|eot_id|>
 Here is the Description of a dataset. 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 a' . Please return only a json list of at most 3 Keywords corresponding to the 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 a
air quality; AOD; geostationary satellite; GOCI; machine learning; particulate matter; PM2.5; remote sensing