Reply on RC2 DOI Creative Commons

O Sungmin

Published: Dec. 16, 2024

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch 2020, GEMS has provided hourly daytime information over Asia. However, to date, validation and applications of these data are lacking. Here we evaluate effectiveness 1.5-year aerosol optical depth (AOD) estimating ground-level particulate matter (PM) concentrations at an scale. To do so, employ random forest models use AOD meteorological variables as input features estimate PM10 PM2.5 concentrations, respectively, South Korea. model-estimated PM strongly correlated with ground measurements, but they exhibit negative biases, particularly during high loading months. Our results indicate that values represent underestimates compared ground-measured values, possibly leading biases final estimates. Further, demonstrate more training could significantly improve model performance, thus indicating potential high-resolution surface prediction when sufficient accumulated coming years. will serve a reference aid evaluation future retrieval algorithm improvements also provide initial guidance users.

Language: Английский

Reply on AC1 DOI Creative Commons

O Sungmin

Published: Jan. 3, 2025

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch 2020, GEMS has provided hourly daytime information over Asia. However, to date, validation and applications of these data are lacking. Here we evaluate effectiveness 1.5-year aerosol optical depth (AOD) estimating ground-level particulate matter (PM) concentrations at an scale. To do so, employ random forest models use AOD meteorological variables as input features estimate PM10 PM2.5 concentrations, respectively, South Korea. model-estimated PM strongly correlated with ground measurements, but they exhibit negative biases, particularly during high loading months. Our results indicate that values represent underestimates compared ground-measured values, possibly leading biases final estimates. Further, demonstrate more training could significantly improve model performance, thus indicating potential high-resolution surface prediction when sufficient accumulated coming years. will serve a reference aid evaluation future retrieval algorithm improvements also provide initial guidance users.

Language: Английский

Citations

0
Deep Learning Calibration Model for PurpleAir PM2.5 Measurements: Comprehensive Investigation of the PurpleAir Network DOI
Masoud Ghahremanloo, Yunsoo Choi, Mahmoudreza Momeni

et al.

Atmospheric Environment, Journal Year: 2025, Volume and Issue: unknown, P. 121118 - 121118

Published: Feb. 1, 2025

Language: Английский

Citations

0
Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach DOI Creative Commons

O Sungmin,

Ji Won Yoon, Seon Ki Park

et al.

Atmospheric measurement techniques, Journal Year: 2025, Volume and Issue: 18(6), P. 1471 - 1484

Published: March 28, 2025

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch 2020, GEMS has provided hourly daytime information over Asia. However, to date, validation and applications of these data are largely lacking. Here we evaluate effectiveness 2 years aerosol optical depth (AOD) estimating ground-level particulate matter (PM) concentrations at an scale. To do so, train random forest XGBoost machine learning algorithms using AOD meteorological variables as input features, then employ trained models estimate PM10 PM2.5 South Korea. model-estimated PM capture spatial temporal variations observed ground-based measurements well, showing strong correlations. they exhibit noticeable biases extremes, with a tendency overestimate lower levels underestimate them higher levels. Incorporating locally available data, such carbon monoxide nitrogen dioxide measurements, into model training further enhances performance, improving correlations reducing errors. Moreover, demonstrate feasibility neighbouring station ungauged locations where ground not available. Our results will serve reference aid evaluation future retrieval algorithm improvements also provide initial guidance users.

Language: Английский

Citations

0
Tropospheric NO2 Column over Tibet Plateau According to Geostationary Environment Monitoring Spectrometer: Spatial, Seasonal, and Diurnal Variations DOI Creative Commons
Xue Zhang,

Chunxiang Ye,

Jhoon Kim

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(10), P. 1690 - 1690

Published: May 12, 2025

Nitrogen oxides (NOx) are key precursors of tropospheric ozone and particulate matter. The sparse local observations make it challenging to understand NOx cycling across the Tibetan Plateau (TP), which plays a crucial role in regional global atmospheric processes. Here, we utilized Geostationary Environment Monitoring Spectrometer (GEMS) data examine NO2 vertical column density (ΩNO2) spatiotemporal variability over TP, pristine environment marked with natural sources. GEMS revealed that ΩNO2 TP is generally low compared surrounding regions significant surface emissions, such as India Sichuan basin. A spatial decreasing trend observed from south center north Tibet. Unlike regions, exhibits opposing seasonal patterns negative correlation between ΩNO2. In Lhasa Nam Co areas within Xizang, highest spring contrasts lowest concentration. Diurnally, midday increase warm season reflects some external sources affecting remote area. Trajectory analysis suggests strong convection lifted air mass Southeast Asia into upper troposphere TP. These findings highlight mixing interplay nonlocal shaping high-altitude environment. Future research should explore these transport mechanisms their implications for chemistry climate dynamics

Language: Английский

Citations

0
First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns DOI Creative Commons
Jincheol Park, Yunsoo Choi, Jia Jung

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 17, 2024

Pioneering the use of Geostationary Environment Monitoring Spectrometer's (GEMS) observation data in air quality modeling, we adjusted Asia's NO

Language: Английский

Citations

3
Estimating hourly ground-level aerosols using GEMS aerosol optical depth: A machine learning approach DOI Creative Commons

O Sungmin,

Ji Won Yoon, Seon Ki Park

et al.

Published: Aug. 26, 2024

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch 2020, GEMS has provided hourly daytime information over Asia. However, to date, validation and applications of these data are lacking. Here we evaluate effectiveness 1.5-year aerosol optical depth (AOD) estimating ground-level particulate matter (PM) concentrations at an scale. To do so, employ random forest models use AOD meteorological variables as input features estimate PM10 PM2.5 concentrations, respectively, South Korea. model-estimated PM strongly correlated with ground measurements, but they exhibit negative biases, particularly during high loading months. Our results indicate that values represent underestimates compared ground-measured values, possibly leading biases final estimates. Further, demonstrate more training could significantly improve model performance, thus indicating potential high-resolution surface prediction when sufficient accumulated coming years. will serve a reference aid evaluation future retrieval algorithm improvements also provide initial guidance users.

Language: Английский

Citations

0
Reply on RC2 DOI Creative Commons

O Sungmin

Published: Dec. 16, 2024

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch 2020, GEMS has provided hourly daytime information over Asia. However, to date, validation and applications of these data are lacking. Here we evaluate effectiveness 1.5-year aerosol optical depth (AOD) estimating ground-level particulate matter (PM) concentrations at an scale. To do so, employ random forest models use AOD meteorological variables as input features estimate PM10 PM2.5 concentrations, respectively, South Korea. model-estimated PM strongly correlated with ground measurements, but they exhibit negative biases, particularly during high loading months. Our results indicate that values represent underestimates compared ground-measured values, possibly leading biases final estimates. Further, demonstrate more training could significantly improve model performance, thus indicating potential high-resolution surface prediction when sufficient accumulated coming years. will serve a reference aid evaluation future retrieval algorithm improvements also provide initial guidance users.

Language: Английский

Citations

0