Yuxuan Wang

Hot extremes have increased globally and on a regional scale. Traditional data processing methods to understand air quality changes in hot extremes may not adequately describe variations in directly emitted air pollutants like nitrogen oxides (NO _x ) or volatile organic compounds (VOCs) and their influence on secondary species such as ozone (O ₃ ). In Houston, TX (USA), the TROPOspheric Monitoring Instrument (TROPOMI) NO ₂ and HCHO columns observed an increase of 4.92% and 21.25%, respectively, in hot extremes, but the increase and resulting vulnerability on populations were not uniform spatially and temporally, which can go unrecognized. A clustering algorithm CLASP (CLustering of Atmospheric Satellite Products) was applied to capture variations and describe high magnitude features in TROPOMI observations on their spatial, magnitude, and temporal axes. CLASP qualified variations of NO ₂ and HCHO in hot extremes driven by biogenic and anthropogenic variables, by identifying increases in the magnitude and frequency of high magnitude hotspots in Houston. Population‐weighted TROPOMI columns together with CLASP inform how communities of color observe higher weighted population columns and experience a higher frequency and magnitude of NO ₂ and HCHO columns intensifying in hot extremes. Taking an observational approach to describe ozone air quality, we show how CLASP can help inform of variation captured in the TROPOMI column and identify limitations, highlighting the need to clarify how hot extremes affect ozone air quality in regions which may face exacerbated extremes.

Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. This study aims at constraining drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought condition compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought condition compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. While there have been some efforts to reduce uncertainties in isoprene emissions under the drought conditions, the effort has not been made in the South Korean region. Here, we aimed to constrain drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought conditions compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought conditions compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. While there have been some efforts to reduce uncertainties in isoprene emissions under the drought conditions, the effort has not been made in the South Korean region. Here, we aimed to constrain drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought conditions compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought conditions compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. While there have been some efforts to reduce uncertainties in isoprene emissions under the drought conditions, the effort has not been made in the South Korean region. Here, we aimed to constrain drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought conditions compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought conditions compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Prolonged high temperature events (hot extremes) have increased globally and on a regional scale. Traditional data processing methods to understand air quality changes in hot extremes may not adequately describe variations in directly emitted air pollutants like nitrogen oxides (NOx) or volatile organic compounds (VOCs) and then their influence on secondary species like ozone (O3). In Houston, TX (USA), the TROPOMI NO2 (4.92%) and HCHO (21.25%) column observe an increase in hot extremes, but spatially and temporally the increase and resulting air quality vulnerability on populations is not uniform and can go unrecognised. Lee and Wang (2023) developed a clustering algorithm CLASP (CLustering of Atmospheric Satellite Products) rooted in density-based clustering which can capture variation and describe high magnitude features captured in TROPOMI observations on their spatial, magnitude, and temporal axes. CLASP qualified variation in hot extremes driven by biogenic and anthropogenic variables, by identifying increases in the magnitude and frequency of high magnitude hotspots in Houston. CLASP readily identified unique column increases in hot extremes which could be attributed to a regional airport and a peaking power plant. Population-weighted TROPOMI columns together with CLASP inform how Hispanics and Blacks or African Americans observe higher weighted population columns, and that these groups experience a higher frequency and magnitude of NO2 and HCHO compared to other population groups, which intensifies in hot extremes. Surface ozone sees an overall increase of 14.80% in hot extremes, but similarly, increases are not homogenous as different extreme periods see varying degrees of change. In taking an observational approach to describe ozone air quality, we show how CLASP can help inform of variation captured in the TROPOMI column as well as identified limitations which prompts a need to further untangle how hot extremes effect ozone air quality in a region that may experience further exacerbated extremes.

Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. While there have been some efforts to reduce uncertainties in isoprene emissions under the drought conditions, the effort has not been made in the South Korean region. Here, we aimed to constrain drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought conditions compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought conditions compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. While there have been some efforts to reduce uncertainties in isoprene emissions under the drought conditions, the effort has not been made in the South Korean region. Here, we aimed to constrain drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought conditions compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought conditions compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. While there have been some efforts to reduce uncertainties in isoprene emissions under the drought conditions, the effort has not been made in the South Korean region. Here, we aimed to constrain drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought conditions compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought conditions compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.