ZHAO-CHENG ZENG (曾招城)
Dr. Zhao-Cheng Zeng is currently an Assistant Researcher at JIFRESSE of UCLA, and a Visiting Researcher at Caltech. He was a postdoc researcher at Caltech for two years after finishing his PhD from CUHK in Hong Kong.
- Remote sensing of atmospheric greenhouse gases and aerosols To retrieve atmospheric trace gases (e.g., CO2, CH4, and their isotopes) and aerosols (optical properties and profiling) over land and ocean using hyper-spectral remote sensing measurements from space.
- Remote sensing and modeling of urban emissions Using mountain-top and space-borne remote sensing instrument to monitoring megacity carbon emissions and air pollutants and quantify anthropogenic emissions using model simulations by the WRF-Chem.
- 10/2019 OCO-2 aerosol profiling paper accepted by RSE! Online now: https://doi.org/10.1016/j.rse.2019.111494
- 07/2019 GRL paper on methane emissions published! In the news: Caltech News; EurekAlert; ScienceDaily
- 09/2018 GRL paper on aerosol profiling published!
- 06/2018 Talk at IWGGMS-11;
- 05/2018 Mars methane detection paper published!
Five representative publications
 He, L., Z.-C. Zeng (co-first author), T. Pongetti, C. Wong, J. Liang, K. R. Gurney, et al., 2018, Atmospheric methane emissions correlate with natural gas consumption from residential and commercial sectors in Los Angeles, Geophysical Research Letter, doi: 10.1029/2019GL083400. [PDF]
 Z. C. Zeng, V. Natraj, F. Xu, T. J. Pongetti, R.-L. Shia, E. A. Kort, et al., (2018). Constraining Aerosol Vertical Profile in the Boundary Layer Using Hyperspectral Measurements of Oxygen Absorption. Geophysical Research Letter, DOI: 10.1029/2018GL079286. [PDF]
 Z.-C. Zeng, Q. Zhang, V. Natraj, J. Margolis, R.-L. Shia, S. Newman, et al., (2017), “Aerosol Scattering Effects on Water Vapor Retrievals over the Los Angeles Basin,” Atmospheric Chemistry and Physics, DOI:10.5194/acp-2016-490. [PDF]
 Z.-C. Zeng, L. Lei, K. Strong, D. B. A. Jones, L. Guo, M. Liu, et al., (2017), “Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics,” International Journal of Digital Earth, 10(4), DOI: 10.1080/17538947.2016.1156777. [PDF]
 Z.-C. Zeng, L. Lei, S. Hou, F. Ru, X. Guan, and B. Zhang (2014), “A Regional Gap-Filling Method Based on Spatio-temporal Variogram Model of CO2 Columns,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 5, DOI: 10.1109/TGRS.2013.2273807. [PDF]
1. MOnitoring urban emissions
Carbon emissions from cities represent the single largest human contribution to climate change. Here we used a mountain-top observatory to monitor the diurnal, seasonal, and inter-annual variabilities of CO2, CH4, CO, N2O, and the aerosol loadings in the Los Angeles megacity.
- Zeng et al. (2019d);
- He, Zeng, et al. (2019);
2. Profiling air pollutants from space
Satellite measurements provide little or no information on the vertical distribution of aerosols. In particular, there is poor measurement of aerosols in the planetary boundary layer (PBL), the part of the atmosphere closest to the surface. In this study, we develop an algorithm to retrieve the vertical structure of aerosols in PBL using remote sensing.
- Zeng et al. (2018)
- Zeng et al. (2019a)
3. Spatio-temporal Statistics for environmental science
The number of available XCO2 retrievals from space is irregularly distributed in space and time, which make it difficult to directly interpret their scientific significance. I developed an interpolation method for regional and global mapping of xCO2 from space based on spatio-temporal geostatistics.
- Zeng et al. (2013);
- Zeng et al. (2014);
- Zeng et al. (2017).
4. Urban Street Sensing
View factors for sky, trees, and buildings are three important parameters of the urban outdoor environment . This study develops an approach for accurately estimating sky view factor (SVF), tree view factor (TVF), and building view factor (BVF) of street canyons in the high-density urban environment o using publicly available Google Street View (GSV) images and a deep-learning algorithm.
- Gong, Zeng, et al. (2018)
- Gong, Zeng, et al. (2019)