Research

  • Source Appointment of PM2.5 in Northern China

The first project I was involved in was using a chemical transport model (CTM) to carry out a source appointment of PM2.5 in Northern China. In this project, I learned some basic knowledge of atmospheric chemistry and physics and gained experience in complex numerical modeling (WRF-CMAQ/ISAM). There are some peer-reviewed papers from our lab on this topic.

  1. Mehmood, K., Wu, Y., Wang, L., Yu, S., Li, P., Chen, X., Li, Z., Zhang, Y., Li, M., Liu, W., Wang, Y., Liu, Z., Zhu, Y., Rosenfeld, D., and Seinfeld, J. H. (2020). Relative effects of open biomass burning and open crop straw burning on haze formation over central and eastern China: modeling study driven by constrained emissions, Atmos. Chem. Phys., 20, 2419–2443.


  1. Zhang, Y., Chen, X., Yu, S., Wang, L., Li, Z., Li, M., Liu, W., Li, P., Rosenfeld, D., and Seinfeld, J.H. (2021) City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis. Environmental Pollution 274.

  • Changes in China Air Pollution during COVID-19 Padamic

During the COVID-19 pandemic, most countries have rapidly stopped almost all activities including industry, services, and transportation of goods and people, thus decreasing air pollution in an unprecedented way, and providing a unique opportunity to study air pollutants.

Firstly, We investigated the effects of city lockdowns on air quality based on ground-based observations in both urban and rural areas in Hangzhou. Results indicated that during lockdown (Jan 24 - Feb 15, 2020) relative to the period before lockdown (Jan 1 - 13, 2020), concentrations of PM2.5, PM10, CO and NO2 displayed significantly decreasing trends, while an overall rising trend was observed for O3 in both urban and rural area, which is explained by the 'absence' of NOx.

  1. Wang, L., Li, M., Yu, S., Chen, X., Li, Z., Zhang, Y., Jiang, L., Xia, Y., Li, J., Liu, W., Li, P., Lichtfouse, E., Rosenfeld, D., Seinfeld, J. H. (2020). Unexpected rise of ozone in urban and rural areas, and sulfur dioxide in rural areas during the coronavirus city lockdown in Hangzhou, China: implications for air quality. Environmental Chemistry Letters 18, 1713–1723.

Further, We designed 25 experiments to determine the effects of popularizing electric vehicles (EVs) on air quality during the COVID-19 full lockdown in China based on ground and satellite observations and a chemical transport model (WRF-CMAQ). The results indicated that large traffic flux reductions are near-linear to reductions of NO2 and PM2.5 and a full conversion to EVs can help to reduce 30%-70% of PM2.5 and 30-80% of NO2 across China, suggesting significant environmental benefits.

  1. Wang, L., Chen, X., Zhang, Y., Li, M., Li, P., Jiang, L., Xia, Y., Li, Z., Li, J., Wang, L., Hou, T., Liu, W., Rosenfeld, D., Zhu, T., Zhang, Y., Chen, J., Wang, S., Huang, Y., Seinfeld, J. H., Yu, S. (2021). Switching to electric vehicles can lead to significant reductions of PM2.5 and NO2 across China. One Earth 4, 1037–1048.


  • Smart Environmental Protection (road emissions) over Xiaoshan district in Hangzhou, China

As part of the smart city construction project in Hangzhou, we designed two scenarios (i.e., vehicle exhaust inspection exemption and hyper-fine resolution mapping of urban vehicle emissions) for the public and government respectively, based on Hangzhou's City Brain and Intelligent Transportation System as well as comprehensive traffic monitoring. Here is the demo of the display platform for this project (Only part of the data can be displayed in this demo due to the restrictions of the government intranet).

  1. A two-stage ensemble model framework (XGBoost, Random Forest, and Neural Network) and on-road remote sensing were conducted to assess the on-road vehicle-specific emissions (HC, NO, and CO) and to identify the “dirty” (2.33%) and “clean” (74.92%) vehicles in the real world over Xiaoshan district in the Yangtze River Delta region.

  1. Xia, Y., Jiang, L., Wang, L., Chen, X., Ye, J., Hou, T., Wang, L., Zhang, Y., Li, M., Li, Z., Song, Z., Jiang, Y., Liu, W., Li, P., Rosenfeld, D., Seinfeld, J. H., Yu, S. (2021).Rapid Assessments of LightDuty Gasoline Vehicle Emissions Using On-Road Remote Sensing and Machine Learning. Submitted to Atmos. Chem. Phys. (Initial submition）

2. We established a hyperfine-resolution (10m-1km) bottom-up model framework to calculate primary on-road vehicle emissions (e.g., CO, HC, NOx, and PM2.5 ) and to investigate the effectiveness of routine control strategies on on-road vehicle emission mitigation over Xiaoshan district in the Yangtze River Delta region.

We also predicted the short-term spatiotemporal (i.e., 2946 road links, 24 hours) evolution of on-road vehicle emissions using the STW-XGBoost (Spatiotemporal Weighted XGBoost) algorithm.

  1. Jiang, L., Xia, Y., Wang, L., Chen, X., Ye, J., Hou, T., Wang, L., Zhang, Y., Li, M., Li, Z., Song, Z., Jiang, Y., Liu, W., Li, P., Rosenfeld, D., Seinfeld, J. H., Yu, S. (2021). Hyperfine-Resolution Mapping of On-Road Vehicle Emissions with Comprehensive Traffic Monitoring and Intelligent Transportation System. Atmos. Chem. Phys. （Preprint)