Home

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.

Research Interests

  • 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.


UPDATES:


Five representative publications

[5] 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]

[4] 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]

[3] 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]

[2] 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]

[1] 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]

RESEARCH HIGHLIGHTS

1. MOnitoring urban emissions

image source: JPL



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.

Related publications:

  • Zeng et al. (2019d);
  • He, Zeng, et al. (2019);

2. Profiling air pollutants from space

image source: Smog inundates Los Angeles, while clear skies are seen above the city. Credit: Getty Images



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.

Related publications:

  • Zeng et al. (2018)
  • Zeng et al. (2019a)

3. Spatio-temporal Statistics for environmental science

image source: Zeng et al., (2017)




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.

Related publications:

  • Zeng et al. (2013);
  • Zeng et al. (2014);
  • Zeng et al. (2017).

4. Urban Street Sensing

image source: Gong et al., (2018)



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.

Related publications:

  • Gong, Zeng, et al. (2018)
  • Gong, Zeng, et al. (2019)