Welcome to the Occupational and Environmental Health & Artificial Intelligence Lab (OEH-AI)!

The OEH-AI is a multidisciplinary research laboratory founded at the University of Oklahoma Health Sciences Center in 2018. Research in the lab focuses on:

  • Identifying and assessing exposure of hazards (e.g., aerosols, bioaerosols, and gases)
  • Integrating artificial intelligence techniques (e.g. machine learning) into occupational and environmental health fields
  • Studying air pollutants and their climate effects using atmospheric dynamic models (e.g. WRF-Chem, the Weather Research and Forecasting model coupled with Chemistry)

 

For more information, or if you are interested in participating in our research studies, please contact the Lab PI Dr. Changjie Cai at (405)271-2070, Ext. 46774 or email at changjie-cai@ouhsc.edu

Faculty:

Changjie Cai, PhD, OEH-AI Lab PI, Assistant Professor, Department of Occupational and Environmental Health

Tianbao Yang, PhD, Associate Professor, Department of Computer Science at the University of Iowa

Thomas Peters, PhD,CIH, Professor, Department of Occupational and Environmental Health at the University of Iowa

David L. Johnson, PhD,PE,CIH,BCEE, Professor, Department of Occupational and Environmental Health

Xiao-Ming Hu, PhD, Senior Research Scientist and Ajunct Associate Professor, School of Meteorology

Fernando Suárez López del Amo, DDS, MS, Assistant Professor at the Department of Periodontics

 

Students:

Xixi Wu, BDS,MDent,PhD: resident

Billy Brillis, MS student

Xiaoyan Zhang, Visiting PhD student from Nanjing Univeristy of Information Science and Technology

Shiyu Yang, Visiting MS student from Peking University

 

1. Develop portable and cost-effective devices

We developed an innovative cost-effective portable aerosol collector and spectrometer (PACS) in collecting and measuring aerosols in different metrics, including the number, surface area, and mass concentrations by size from 10 nm to 10 µm. This work includes the development of both hardware and software, and laboratory test of the device by comparing to reference instruments.

Related publicaitons:

  1. Cai, C., Stebounova, L.V., Peate, D.W., & Peters, T.M. (2019). Evaluation of a portable aerosol collector and spectrometer to measure particle concentration by composition and size. Aerosol Science and Technology, 1-13.
  2. Cai, C., Thomas, G. W., Yang, T., Park, J. H., Gogineni, S. P., & Peters, T. M. (2018). Development of a Portable Aerosol Collector and Spectrometer (PACS). Aerosol Science and Technology, 52(12), 1351-1369.

 

2. Study air pollutants and their climate effects using WRF-Chem model

We modified and applied regional air quality models to study air pollutants. For example, we incorporated several new nucleation schemes and particle early growth schemes into the Weather Research and Forecasting Model coupled with Chemistry (WRF/Chem). Our work considered the different nucleation schemes in various heights and areas, and also extend the minimal size bin of 39 nm to 1 nm. According to the model evaluations, our work greatly improved the model’s capabilities of simulating new particle formation and particle early growth. We also evaluate the direct and in-direct effects of anthropogenic aerosols. 

Related publications:

  1. Cai, C., Zhang, X., Wang, K., Zhang, Y., Wang, L., Zhang, Q., ... & Yu, S. C. (2016). Incorporation of new particle formation and early growth treatments into WRF/Chem: Model improvement, evaluation, and impacts of anthropogenic aerosols over East Asia. Atmospheric Environment, 124, 262-284.
  2. Cai, C. (2013). Performance Evaluation and Testing of New Particle Formation and Growth Parameterizations Using Two Regional Online-Coupled Meteorology-Chemistry Models.

 

3. Predict air pollutant using machine learning methods

We applied the machine learning method to improve the air quality prediction.

Related publications:

  1. Zhu, D., Cai, C., Yang, T., & Zhou, X. (2018). A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization.

Inventory:

Scanning Mobility Particle Sizer (SMPS) Spectrometer (Model 3936)

Ultraviolet Aerodynamic Particle Sizer (UV-APS) Spectrometer (Model 3314)

NanoMOUDI-II Impactors (Model 125 R)

Spark Discharging System

 

Scanning Mobility Particle Sizer (SMPS) Spectrometer (Model 3936):

The SMPS can continuesly measure particle number concentration by size (64 size bins per decade) from 0.010 to 0.6 µm (equivalent mobility diameters) in near real-time.

 

Ultraviolet Aerodynamic Particle Sizer (UV-APS) Spectrometer (Model 3314):

The UV-APS can measure fluorescence plus aerodynamic size and scattered-light intensity. It can continuesly measure particle number concentration by size (32 size bins per decade) from 0.5 to ~20 µm (aerodynamic diameters) in near real-time.

 

NanoMOUDI-II Impactors (Model 125 R):

The NanoMOUDI can collect particles by size onto substrates for subsequent physical and chemical analysis. The nomial cut point diameters are 0.010, 0.018, 0.032, 0.056, 0.10, 0.18, 0.32, 0.56, 1.0, 1.8, 3.2, 5.6 and 10 µm. 

   

 

Spark Discharging System:

This lab-made Spark Discharging System can generate stable metal nano-particles by size and composition.

Cai, C., Stebounova, L.V., Peate, D.W., & Peters, T.M. (2019). Evaluation of a portable aerosol collector and spectrometer to measure particle concentration by composition and size. Aerosol Science and Technology, 53(6), 675-687.

Cai, C., Thomas, G. W., Yang, T., Park, J. H., Gogineni, S. P., & Peters, T. M. (2018). Development of a Portable Aerosol Collector and Spectrometer (PACS). Aerosol Science and Technology, 52(12), 1351-1369.

Zhu, D., Cai, C., Yang, T., & Zhou, X. (2018). A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization. Big Data and Cognitive Computing, 2(1), 5.

Anthony, T. R., Cai, C., Mehaffy, J., Sleeth, D., & Volckens, J. (2017). Performance of prototype high-flow inhalable dust sampler in a livestock production facility. Journal of occupational and environmental hygiene, 14(5), 313-322.

Cai, C., Zhang, X., Wang, K., Zhang, Y., Wang, L., Zhang, Q., ... & Yu, S. C. (2016). Incorporation of new particle formation and early growth treatments into WRF/Chem: Model improvement, evaluation, and impacts of anthropogenic aerosols over East Asia. Atmospheric Environment, 124, 262-284.

Xia, L., Cai, C., Zhu, B., An, J., Li, Y., & Li, Y. (2014). Source apportionment of VOCs in a suburb of Nanjing, China, in autumn and winter. Journal of Atmospheric Chemistry, 71(3), 175-193.

Cai, C. (2013). Performance Evaluation and Testing of New Particle Formation and Growth Parameterizations Using Two Regional Online-Coupled Meteorology-Chemistry Models.

Tie, X., Geng, F., Guenther, A., Cao, J., Greenberg, J., Zhang, R., ... & Cai, C. (2013). Megacity impacts on regional ozone formation: observations and WRF-Chem modeling for the MIRAGE-Shanghai field campaign. Atmospheric Chemistry and Physics, 13(11), 5655-5669.

Chen, Y., Liu, Q., Geng, F., Zhang, H., Cai, C., Xu, T., ... & Li, H. (2012). Vertical distribution of optical and micro-physical properties of ambient aerosols during dry haze periods in Shanghai. Atmospheric Environment, 50, 50-59.

Friedli, H. R., Arellano Jr, A. F., Geng, F., Cai, C., & Pan, L. (2011). Measurements of atmospheric mercury in Shanghai during September 2009. Atmospheric Chemistry and Physics, 11(8), 3781-3788.

Cai, C., Geng, F., Tie, X., Yu, Q., & An, J. (2010). Characteristics and source apportionment of VOCs measured in Shanghai, China. Atmospheric Environment, 44(38), 5005-5014.

Cai, C. J., Geng, F. H., Tie, X. X., Yu, Q., Peng, L., & Zhou, G. Q. (2010). Characteristics of ambient volatile organic compounds (VOCs) measured in Shanghai, China. Sensors, 10(8), 7843-7862.

Cai, C. J., Geng, F. H., Yu, Q., An, J. L., & Han, J. J. (2010). Source apportionment of VOCs at city centre of Shanghai in summer. Acta Scientiae Circumstantiae, 30(5), 926-934.

Geng, F., Cai, C., Tie, X., Yu, Q., An, J., Peng, L., ... & Xu, J. (2009). Analysis of VOC emissions using PCA/APCS receptor model at city of Shanghai, China. Journal of atmospheric chemistry, 62(3), 229-247.

  • OEH 5752: Hazards Control
  • OEH 5013: Environmental Health
  • Aerosol Technology
  • Assessing Physical Agent Hazards