Our teaching integrates fundamental scientific principles with advanced computational methods, satellite remote sensing, and artificial intelligence to prepare undergraduate and graduate students to address complex challenges in atmospheric and environmental sciences.

• EVR 6216 / EVR 4232: Atmospheric Composition and Remote Sensing

Explores the chemical composition of Earth's atmosphere, atmospheric processes, and the principles of satellite remote sensing. Students learn how satellite observations, ground-based measurements, and atmospheric models are used to investigate air pollution, atmospheric chemistry, and environmental change.

• EVR 6135 / EVR 4135: Machine Learning for Geosciences

Introduces machine learning and artificial intelligence applications in Earth and environmental sciences. Topics include supervised and unsupervised learning, neural networks, deep learning, and explainable AI. Students develop practical skills in analyzing geospatial and environmental datasets, identifying complex patterns, and building predictive models for geoscience applications.

• EVR 6115 / EVR 4114: Climate Change

Examines the physical and chemical processes governing Earth's climate system, natural and anthropogenic drivers of climate change, and the application of climate models. The course also explores the environmental, economic, and societal consequences of climate change, including extreme events, adaptation, and mitigation strategies.

• CHM 2210 / CHM 2211: Organic Chemistry

Covers the fundamental principles of organic chemistry, including molecular structure, bonding, reaction mechanisms, stereochemistry, and the properties and reactivity of organic compounds. Emphasis is placed on understanding chemical processes and their applications across environmental, biological, and chemical sciences.

At ACCL, we develop and apply advanced artificial intelligence (AI), machine learning (ML), deep learning, and explainable AI (XAI) techniques to address complex challenges in atmospheric and environmental sciences. By integrating satellite remote sensing, ground-based observations, meteorological reanalysis, Earth system models, and socioeconomic datasets, we investigate nonlinear relationships and complex interactions among atmospheric composition, air quality, extreme environmental hazards, and human health. Our research employs data-driven and physics-informed approaches for environmental prediction, spatiotemporal analysis, uncertainty quantification, and causal discovery. We also integrate AI with economic and societal impact assessment models to evaluate the consequences of environmental changes and extreme events. These interdisciplinary approaches aim to advance scientific understanding, improve predictive capabilities, and translate environmental data into actionable information that supports public health, environmental management, and evidence-based decision-making.

At ACCL, we investigate the socioeconomic impacts of extreme hazards by integrating physical models, economic assessment frameworks, and artificial intelligence (AI) techniques. Our research focuses on quantifying economic damages, evaluating adaptation strategies, and assessing risks to infrastructure and coastal communities under varying climate and socioeconomic scenarios. We employ three principal modeling frameworks: the Policy Analysis for the Greenhouse Effect (PAGE), the Infrastructure Planning Support System (IPSS), and the Coastal Impact and Adaptation Model (MimiCIAM).

1. Coastal Impact and Adaptation Model (MimiCIAM): The Coastal Impact and Adaptation Model (MimiCIAM) is an integrated assessment framework used to quantify the economic damages associated with sea-level rise and evaluate coastal adaptation strategies under different climate and socioeconomic scenarios. The model integrates projections of local sea-level rise, population, economic development, and coastal characteristics to estimate damages and adaptation costs. Our recent research applies MimiCIAM to investigate the economic consequences of sea-level rise across U.S. coastal states and World Bank regions, examining the influence of climate scenarios, socioeconomic development, discount rates, and adaptation decisions. We further integrate machine learning techniques with model simulations to develop computationally efficient predictive tools, identify key drivers of economic damages, and support coastal risk assessment and adaptation planning.
2. PAGE IAM: Policy Analysis for the Greenhouse Effect (PAGE) integrated assessment model (IAM), on top of the @Risk package. This system is used for large scale global assessment of climate on infrastructure. PAGE predicts GMST and [GHG] as prognostic variables on an annual time step, representing complex climate feedbacks as statistical emulators (Figure 1).  Emulators are statistical regressions of the bulk effects of a process based on simulations from full-physics GCMs and LSMs. PAGE predicts the global average atmospheric concentration of CO2, CH4, N2O, high warming potential gases, sulphate aerosols, and all other GHGs combined.  Anthropogenic GHG emissions come from the RCPs (van Vuuren et al., 2011) and [CO2] depends on land uptake, ocean uptake, and the PCF.  Land uptake represents the balance between photosynthetic uptake by plants and respiration into the atmosphere due to the decay of dead organic material.  PAGE has several coupled feedbacks, resulting in a non-linear response to anthropogenic GHG radiative forcing.
3. IPSS: The Infrastructure Planning Support System (IPSS) is an integrated system that determines vulnerabilities to climate change, the costs associated with these vulnerabilities and possible adaptation strategies, and finally, the risk associated with adopting either a proactive adaptation strategy or a reactive “business-as-usual” strategy. We integrate the IPSS system with sustained stakeholder engagement to discover new ways of integrating multi-scalar, interdisciplinary datasets. This interdisciplinary approach is compatible with the new IPCC recommendation to mitigate the climate change consequences in Coastal cities (IPCC, 2019).

We use multiscale models that range from explicit 0D box models to 3D models. We use the following models for atmospheric chemistry investigations:

1. 0D box model:
   We use box models to investigate and elucidate the atmospheric chemical mechanisms during extensive field and laboratory measurements. In these simulations, we use explicit chemical mechanisms such as the master chemical mechanism (MCMv3.3), which contains ~20,000 reactions and ~8000 species. Output from these explicit models help us understand the radical budget in the atmosphere such
   
   
   Figure 3: Average daytime fluxes of the radical sources and sinks for the city of Santiago de Chile calculated by the MCM model during winter (bold) and summer (between brackets). Units are in ppbv h-1. HONO net contribution, 82% (52%) in this diagram is slightly lower than in the text because [HONO]PSS is calculated here using [OH] calculated by the MCM (3–4% higher). Adopted from Elshorbany et al., 2010.
   
   
2. 3D CTM:
   We use 3D chemical transport models (CTM) to investigate the global impact of new or important chemical mechanisms. W currently use the CESM model in the CTM mode for these applications. 
   
   

   Figure 4: Average simulated OH (from top to bottom) (106 molecules cm−3), H2O2, HNO3, H2SO4 (ppbv) near the surface from the reference run (base_S1, left panels) and their relative enhancements (%) due to the implementation of realistic HONO levels in the model (S1, right panels) in January. Adopted from Elshorbany et al., 2014a.
   
   Simulated OH levels using a computation efficient module implemented in the NASA GEOS5 CCM model is also presented below:

   

   Figure 5: Seasonal mean (1988–2007) OH (⇥106 molecules cm−3) for the Base scenario for December–February (DJF) and June–August (JJA) at 850 mbar. Adopted from Elshorbany et al., 2016
   
   

3. Earth System Models (ESM): We also use CESM as a fully coupled atmosphere-land-ocean general circulation model (GCM) to investigate chemistry-climate interactions.

We use several remote sensing products to validate our 3D model output.

Figure 6: Seasonal mean (2006–2007) CO columns (⇥1016 molecules cm−2) from the Base scenario (left column) and the relative difference (%; (Base observations)/observations; right column) with MOPITT data. Adopted from Elshorbany et al., 2016.

Figure 7: Seasonal mean (2004) methane dry column (ppbv; left column) from the Base scenario and the relative difference (%, (Base observations)/observations; right column) with SCIAMACHY data. Simulated methane levels are gridded to the spatial resolution of the SCIAMACHY data. Adopted from Elshorbany et al., 2016.

The ACCL is specially equipped to do NIST (National Institute of Standardization) and EPA (Environmental Protection Agency) TO-17 method-certified quantitative analysis of volatile organic compounds (VOC) using thermal desorption (TD) gas chromatography (GC) mass spectroscopy (MS) techniques.

Figure 8: The thermal desorption, Gas Chromatography Mass Spectroscopy system in the ACCL.

Figure 9: The VOC automatic sampling unit on top of the measurements tower in Bermuda.

Table 2: List of the VOCs measured during the summer campaign in Santiago de Chile. * Compounds included in the MCM model. Adopted from Elshorbany et al., 2009.

Measured VOC can be used to constrain explicit box models to calculate the radical budget and evaluate the atmospheric oxidation capacity (see Figure 3).

Nitrogen Oxides are measured using the Teledyne T200UP Photolytic NO/NO₂/NO_X Analyzer. The instrument uses a patented high-efficiency Blue Light Converter (BLC) coupled with state-of-the-art microprocessor technology to provide a true measurement of ambient nitrogen dioxide (NO₂). The BLC includes the latest technology in high-powered LEDs and a Teflon cell with reflective properties that increase the overall conversion efficiency, which allows for better speciation of lower NO₂ levels. The Instrument detection limit is ~50 ppt.

1. Elshorbany, Y., Ziemke, J., Strode, S., Petetin, H., Kazuyuki Miyazaki, Isabelle De Smedt, Kenneth Pickering, Rodrigo Seguel, Helen Worden, Tamara Emmerichs, Domenico Taraborrelli, Maria Cazorla, Suvarna Fadnavis, Rebecca Buchholz, Benjamin Gaubert, Néstor Rojas, Thiago Nogueira, Thérèse Salameh, and Min Huang, Tropospheric Ozone Precursors: Global and Regional Distributions, Trends and Variability, Atmos Chem. Phys., egusphere-2024-720, 2024.
   
   
2. Elshorbany, Y.; Mixson, S.; Marcum, L.; Salemi, J.L. The Relation between Atmospheric Aerosol Concentration and SARS-CoV-2 Variants’ Infection and Mortality Rates in the United States: A Remote-Sensing Perspective. Sustainability, 16, 3381. https://doi.org/10.3390/su16083381, 2024.
   
   
3. Rodrigo J. Seguel, Lucas Castillo, Charlie Opazo, Néstor Y. Rojas, Thiago Nogueira, María Cazorla, Mario Gavidia-Calderón, Laura Gallardo, René Garreaud, Tomás Carrasco-Escaff, and Yasin Elshorbany, Changes in South American Surface Ozone Trends: Exploring the Influences of Precursors and Extreme Events, ACP, https://doi.org/10.5194/egusphere-2024-328, 2024.
   
   
4. Iturrate-Garcia, M., Salameh, T., Schlauri, P., Baldan, A., Vollmer, M. K., Stratigou, E., Dusanter, S., Li, J., Persijn, S., Claude, A., Holzinger, R., Sutour, C., Macé, T., Elshorbany, Y., Ackermann, A., Pascale, C., and Reimann, S.: Towards a high quality in-situ observation network for oxygenated volatile organic compounds (OVOCs) in Europe: transferring traceability to the International System of Units (SI) to the field, EGUsphere, https://doi.org/10.5194/egusphere-2024-2236, 2024.
   
   
5. Youfeng Wang, Yuting Zhu, Chunxiang Ye1, Xianliang Zhou, Yasin Elshorbany, Matthew Hayden, and Andrew J. Peters, Observational evidence on unknown NOx source and its perturbation on oxidative capacity in the marine boundary layer at Bermuda, Journal of Geophysical Research: Atmospheres, https://doi.org/10.1029/2023JD039582, 2023.
   
   
6. Sydney Kaylor, Yasin Elshorbany*, Jennifer Collins & Robin Rives (20 Nov 2023): Environmental Perceptions of College Students during the COVID-19 Pandemic, The Professional Geographer, https://doi.org/10.1080/00330124.2023.2258425, 2023. 
   
   
7. Robin Rives, Yasin Elshorbany*, Sydney Kaylor, The Relation between Covid 19, Air Quality and Socioeconomic impacts in the US, GeoHealth,  https://doi.org/10.1029/2022GH000735, 2023. 
   
   
8. Callie Mills, Richard Mbatu, Yasin Elshorbany, Determining Methane Uptake in Tropical Agroforestry Soils: A Case for Inclusion in REDD+, Agroforestry Systems, doi: https://doi.org/10.1007/s10457-023-00811-9, 2023.
   
   
9. Elshorbany, Y. F., Yuting Zhu, Youfeng Wang, Xianliang Zhou, Chunxiang Ye, Matthew Hayden and Andrew Peters, Summer Sanderfield, Seasonal Dependence of the Atmospheric oxidation Capacity in the Marine Boundary Layer, Atmospheric Environment 289, 119326, doi: https://doi.org/10.1016/j.atmosenv.2022.119326, 2022.
10. Yuting Zhu, Youfeng Wang, Xianliang Zhou, Yasin F Elshorbany, Chunxiang Ye, Matthew Hayden and Andrew Peters, An investigation into daytime HONO chemistry in the marine boundary layer at Tudor Hill Marine Atmospheric Observatory in Bermuda, https://doi.org/10.5194/acp-2021-893, 2022. 
    
    
11. Yasin Elshorbany, Kapper, H., Ziemke, J., Parr, S.: The Status of Air Quality in the US during the COVID-19 Pandemic, Remote Sensing, 2021, 13, 369. https://doi.org/10.3390/rs13030369, 2021. Student contribution. 
    
    
12. El-Sayed, M., Elshorbany, Y., Koehler, K.: On the impact of COVID-19 pandemic on air quality in Florida, Environmental Pollution, 117451, ISSN 0269-7491, https://doi.org/10.1016/j.envpol.2021.117451, 2021. 
    
    
13. Schwalm, Christopher; Schaefer, Kevin; Fisher, Josha; Huntzinger, Deborah; Elshorbany, Yasin; Fang, Yuanyuan; Hayes, Daniel; Jafarov, Elchin; Michalak, Anna; Piper, Mark; Stofferahn, Eric; Wang, Kang; Wei, Y, Divergence in land surface modeling: Linking spread to structure, Environmental Research Communications, 1, 111004, 2019.
    
    
14. Yumashev, D., Hope C., Schaefer, K., Riemann-Campe, K., Iglesias-Suarez, F., Jafarov, E., Whiteman, G., Young, P., Elshorbany, Y.: Climate policy implications of nonlinear decline of Arctic land permafrost and sea ice, Nature Communications, 10, 1900, 2019.
    
    
15. Overeem, I., E. Jafarov, K. Wang, K. Schaefer, S. Stewart, G. Clow, M. Piper, and Y. Elshorbany, A modeling toolbox for permafrost landscapes, Eos, 99, 2018.
    
    
16. Schultz, M.G., Schröder, S., Lyapina, O., Cooper, O., Galbally, I., Petropavlovskikh, I., von Schneidemesser, E., Tanimoto, H., Elshorbany, Y., et al..: Tropospheric Ozone Assessment Report: Database and Metrics Data of Global Surface Ozone Observations, Elem Sci Anth. 2017; 5, 58.244, 2017.
    
    
17. Meusel, H., Elshorbany, Y., Kuhn, U., Bartels-Rausch, T., Reinmuth-Selzle, K., Kampf, C. J., Li, G., Wang, X., Lelieveld, J., Pöschl, U., Hoffmann, T., Su, H., Ammann, M., and Cheng, Y.: Light-induced protein nitration and degradation with HONO emission, Atmos. Chem. Phys., 17, 11819-11833, 2017.
    
    
18. Elshorbany, Y. F., Duncan, B. N., Strode, S. A., Wang, J. S., and Kouatchou, J.: The description and validation of the computationally Efficient CH4–CO–OH (ECCOHv1.01) chemistry module for 3-D model applications, Geosci. Model Dev., 9, 799-822, doi:10.5194/gmd-9-799-2016, 2016.
    
    
19. Elshorbany, Y. F., Crutzen, P. J., Steil, B., Pozzer, A., Tost, H., and Lelieveld, J.: Global and regional impacts of HONO on the chemical composition of clouds and aerosols, Atmos. Chem. Phys., 14, 1167-1184, doi:10.5194/acp-14-1167-2014, 2014.
    
    
20. Elshorbany, Y. F., Steil, B., Brühl, C., and Lelieveld, J.: Impact of HONO on global atmospheric chemistry calculated with an empirical parameterization in the EMAC model, Atmos. Chem. Phys., 12, 9977-10000, doi:10.5194/acp-12-9977-2012, 2012b.
    
    
21. Elshorbany, Y. F., Kleffmann, J., Hofzumahaus, A., Kurtenbach, R., Wiesen, P., Dorn,H.-P., Schlosser, E., Brauers, T., Fuchs, H., Rohrer, F., Wahner, A., Kanaya, Y., Yoshino, A., Nishida, S., Kajii, Y., Martinez, M., Rudolf, M., Harder, H., Lelieveld, J., , Elste, T., Plass-Dülmer, C., Stange, G., and Berresheim, H.: HOx Budgets during HOxComp: a Case Study of HOx Chemistry under NOx limited Conditions, J. Geoophys. Res., 117, D03307, doi: 10.1029/2011JD017008, 2012a.
    
    
22. Kanaya, Y., Hofzumahaus, A., Dorn, H.-P., Brauers, T., Fuchs, H., Holland, F., Rohrer, F., Bohn, B., Tillmann, R., Wegener, R., Wahner, A., Kajii, Y., Miyamoto, K., Nishida, S., Watanabe, K., Yoshino, A., Kubistin, D., Martinez, M., Rudolf, M., Harder, H., Berresheim, H., Elste, T., Plass-Dülmer, C., Stange, G., Kleffmann, J., Elshorbany, Y., and Schurath, U.: Comparisons of observed and modeled OH and HO2 concentrations during the ambient measurement period of the HOxComp field campaign, Atmos. Chem. Phys., 12, 2567-2585, doi:10.5194/acp-12-2567-2012, 2012.
    
    
23. Niedojadlo, A.; Kurtenbach, R.; Becker, K.-H.; Wiesen, P.; Schady, A.; Sowka, I.; Elshorbany, Y.; Zwozdziak, A.; Zwozdziak, J.: Volatile organic compounds in Wroclaw, Poland, Gefahrstoffe- Reinhaltung der Luft, 1/2, 39-47, 2012.
    
    
24. Elshorbany, Y. F, Barnes, I., Becker, K. H, Kleffmann, J., and Wiesen, P.: Sources and Cycling of Tropospheric Hydroxyl Radicals-An Overview, Zeitschrift für Physikalische Chemie, 224, 967-987, DOI:10.1524/zpch.2010.6136, 2010b.
    
    
25. Elshorbany, Y. F, Kleffmann, J., Kurtenbach, R., Rubio, R., Lissi, E., Villena, G., Rickard, A.R. and Pilling, M. J., Wiesen, P.: Seasonal dependence of the oxidation capacity of the city of Santiago de Chile, Atmospheric Environment, 44, 5383-5394, a special issue dedicated for the Atmospheric Chemical Mechanism conference, ACM-2009, Davis, US, 2010a.
    
    
26. Elshorbany, Y. F., Kurtenbach, R., Wiesen, P. Lissi, E., Rubio, M., Villena, G., Gramsch, E., Rickard, A. R., Pilling, M. J., Kleffmann. J.: Oxidation capacity of the city air of Santiago, Chile, Atmospheric Chemistry and Physics, 9, 2257-2273, 2009a.
    
    
27. Elshorbany, Y. F, Kleffmann, J., Kurtenbach, R., Rubio, R., Lissi, E., Villena, G., Rickard, A.R. and Pilling, M. J., Wiesen, P.: Summertime Photochemical Ozone Formation in Santiago de Chile, Atmospheric Environment, 43, 6398-6420, 2009b.
    
    
28. Bartels-Rausch, T., Brigante, M., Elshorbany, Y. F., Ammann, M., D’Anna, B., George, C., and Kleffmann, J.: Humic acid in ice: Photo-enhanced conversion of nitrogen dioxide into nitrous acid, Atmospheric Environment, 40, 5443-5450, 2010.
    
    
29. Maria A. Rubio, Valeria Vilches, Eduardo Lissi, Guillermo Villena, Yasin F. Elshorbany, Jörg Kleffmann, Ralf Kurtenbach, and Peter Wiesen Rate of nocturnal ozone depletion in downtown Santiago, Chile, Fresenius environmental Bulletin 20, (5) 1277-1282, 2010.
    
    
30. Rubio, M., Lissi, E., Villena, G., Elshorbany, Y. F., Kleffmann. J., Kurtenbach, R., Wiesen, P.: Simultaneous measurements of formaldehyde and nitrous acid in dews and gas phase in the atmosphere of Santiago, Chile, Atmospheric Environment, 43, 6106 – 6109, 2009.
    
    
31. Metzger, A., Dommen, J., Gaeggeler, K., Duplissy, J., Prevot, A. S. H., Kleffmann, J., Elshorbany, Y., Wisthaler, A. and Baltensperger, U.: Evaluation of 1,3,5 trimethylbenzene degradation in the detailed tropospheric chemistry mechanism, MCMv3.1, using environmental chamber data. Atmospheric Chemistry and Physics, 8, 6453-6468, 2008.
    
    
32. Stemmler, K., Ndour, M., Elshorbany, Y., Kleffmann, J., Ammann, M., D’Anna, B., George, C., and Bohn, B.: Induced conversion of nitrogen dioxide into nitrous acid on submicron humic acid aerosol, Atmospheric Chemistry and Physics, 7, 4237- 4248, 2007.
    
    
33. Kleffmann J., Gavriloaiei T., Elshorbany Y., Ródenas M., Wiesen P.: Detection of nitric acid (HNO3) in the atmosphere using the LOPAP technique, Journal of Atmospheric Chemistry, 58 (2), 131-149, 2007.
    
    
34. Bejan, I., Abd El Aal, Y. (Elshorbany Y), Barnes, I., Benter, T., Bohn, B., Wiesen, P. and Kleffmann, J.: The Photolysis of ortho-nitrophenols: a new phase source of HONO, Physical Chemistry physics, 8 (17): 2028-2035, 2006.

The Atmospheric Chemistry, Composition, and AI Laboratory (ACCL) welcomes motivated undergraduate students interested in gaining hands-on research experience in atmospheric chemistry, air quality, satellite remote sensing, environmental data science, and artificial intelligence (AI) applications.

Undergraduate students can participate in our research activities through the following opportunities:

• Undergraduate Research Assistants: Paid research positions may be available through externally funded projects. Students assist with laboratory and field measurements, satellite data analysis, atmospheric modeling, and machine learning applications. Availability depends on project funding and research needs.

• Independent Undergraduate Research: Students interested in developing research skills or completing undergraduate research projects may work under faculty supervision on topics aligned with the laboratory's research areas. Opportunities may include senior research projects, honors theses, and other approved academic research activities.

• Volunteer Research Opportunities: Motivated undergraduate students are welcome to inquire about volunteer opportunities to gain practical experience and training in laboratory techniques, environmental measurements, computational modeling, remote sensing, and AI-based data analysis.

Interested students are encouraged to contact us with a brief description of their academic background, research interests, and relevant skills.

The Atmospheric Chemistry, Composition, and AI Laboratory (ACCL) welcomes motivated graduate students interested in pursuing M.S. or Ph.D. research in atmospheric chemistry, air quality, satellite remote sensing, Earth system modeling, artificial intelligence (AI), machine learning, and environmental data science.

Prospective graduate students may join our laboratory through graduate programs offered by the School of Geosciencesor the Department of Chemistry at the University of South Florida, depending on their academic background, research interests, and program eligibility.

Graduate Research Assistantships (RAs) may be available through externally funded research projects. These positions provide opportunities to participate in interdisciplinary research involving laboratory and field measurements, satellite observations, computational modeling, and AI-driven environmental analysis. Applicants should be enrolled in or eligible for admission to a relevant graduate degree program and possess the academic background and technical skills appropriate for the research project.

Interested students are encouraged to contact us with their CV, academic background, research interests, and a brief description of their relevant experience. The availability of funded positions depends on current research projects and funding.