Research
Last Updated: September 2024
My research aims to advance our understanding of atmospheric chemistry thereby provide insights into air quality management. I use atmospheric chemistry models to interpret observations from satellites, aircraft, and ground-based networks. My primary focus is on fine particulate matter (PM2.5), tropospheric ozone (O3), and nitrogen oxide (NO2). These are not only important air pollutants in urbanized areas but also have considerable impacts on health and climate.
My current work focuses on improving our understanding of background NO2 using satellite instruments and its implication for free tropospheric ozone formation. Meanwhile, I developed a novel satellite-based indicator to diagnose the sensitivity of surface PM2.5 nitrate formation to emissions.
Previously, I earned a BS in Atmospheric Sciences and a double major in Finance from Nanjing University in 2015. I then received a Ph. D. in Atmospheric Physics and Atmospheric Environment under Prof. Hong Liao from the Institute of Atmospheric Physics, Chinese Academy of Sciences, in 2020. During my Ph. D., my work primarily focused on using a chemical transport model to better understand the long-term changes in PM2.5 and O3 pollution in China. This included separating the contributions of meteorology and emissions, and evaluating their impacts on health and climate.
Ongoing research
Generate a high-resolution cloud-sliced free tropospheric NO2 from geostationary satellite instrument TEMPO
Free tropospheric (FT) nitrogen dioxide (NO2) is a major driver of global oxidant chemistry. It also contributes a background tropospheric NO2column that must be accounted for when interpreting satellite observations. We develop a new TEMPO cloud-sliced FT NO2 seasonal product at 2° × 2.5° spatial resolution and diurnal resolution over North America. We show that the higher data density provided by TEMPO greatly improves the seasonal FT NO2 measurements compared to LEO observations. We compare our TEMPO free tropospheric NO2 product to the GEOS-CF model and find good agreement for seasonality, spatial patterns, and diurnal variation. Discrepancies suggest model errors in parameterizing lightning NOx emissions. The next step goal is to better understand the sources of background NOx and its implications for background tropospheric O3.
Understand the increasingly important role of background NO2
Tropospheric NO2, measured from satellites, has been widely used to track anthropogenic NOx emissions. However, its retrieval and interpretation can be complicated by the free tropospheric NO2 background (above approximately 2 km altitude). Over the US, the background contribution to the tropospheric NO2 column is significant (up to 60%) and increasing. In this study, we use the global transport model GEOS-Chem to better understand the magnitude and trends of free tropospheric background NO2, and the implications for the retrieval and interpretation of satellite data. We find that previous models' underestimation of the background can be largely corrected by considering aerosol nitrate photolysis. A combination of decreasing surface NOx emissions and increasing aircraft emissions is expected to drive a 14% increase in the Air Mass Factor (AMF) over the next decade, which will need to be accounted for in interpreting satellite NO2 trends. Wildfires have contributed to the flattening of OMI NO2 trends over the western US since 2009.
Diagnose the sensitivity of nitrate formation to emissions using satellite observations
We present a novel application of satellite remote sensing to better understand the causes of particulate nitrate pollution. Particulate nitrate is a major air pollutant throughout the urbanized world. Its formation is varyingly sensitive to emissions of nitrogen oxides (NOx), ammonia (NH3), and volatile organic compounds (VOCs). Understanding which of NOx, NH3, or VOC emissions is most important in driving nitrate formation is critical for air quality management. We show that satellite measurements of the NH3/NO2 column ratio along with NO2 columns is an effective indicator to determine the dominant sensitivity regime (NOx-, NH3-, or VOC-sensitive). We find that the dominant sensitivity regimes vary across regions and remain largely consistent across seasons. Our satellite-based indicator provides a simple tool for air quality managers to choose emission control strategies for decreasing PM2.5 nitrate pollution.
Past research
Investigate the distinct roles of meteorology and anthropogenic emissions in the trends of air pollutants
Air pollutant concentrations, including PM2.5 and ozone, are influenced by both meteorological conditions and emissions. Distinguishing between these two factors is crucial for effective air quality management. In our study, we determine their respective contributions by conducting sensitivity simulations using a chemical transport model GEOS-Chem. This research includes two key projects. The first examines the long-term changes (1985-2017) in severe winter haze pollution events in the Beijing-Tianjin-Hebei region, which is the most polluted region in China. The second project investigates surface ozone concentrations during summertime in China’s polluted regions, with a focus on a special emission control period from 2012 to 2017. Both studies highlight the significant influence of meteorological factors and changes in anthropogenic emissions on the long-term trends of air pollutants.
Examine the impacts of air pollution on health and climate
Stringent clean air actions have been implemented in China since 2013, aiming to improve air quality. As a result, observed concentrations of aerosols decreased while those of O3 increased in eastern China. In this work we assess the radiative forcing and health impact resulted from the changes in aerosols and O3 in China for the period of 2012–2017 by using a chemical transport model. The model reproduces the observed changes in PM2.5 and O3 in recent years and estimates a net positive radiative forcing of 1.26 W m-2 in 2017 relative to 2012 over eastern China. Further estimates from the mortality model show that approximately 270,000 deaths are avoided. Results from our study suggest an appreciable health benefit as well as a potential warming as a consequence of clean air actions over 2012–2017.
