• h-index: 13 (Google Scholar)
  • Selected recorded seminars, general talks, and press can be found on my Media page

Submitted and Under Review (advisee)

Feng, X., L.J. Mickley, J.O. Kaplan, M. Kelp, Y. Li, T. Liu. Large role of anthropogenic climate change in driving smoke exposure across the western United States from 1992 to 2020. (Submitted) preprint

Hickman, S., M. Kelp, P. Griffiths, K. Doerksen, K. Miyazaki, E. Pennington, G. Koren, F. Iglesias-Suarez, M. Schultz, K. Chang, O.R. Cooper, A. Archibald, R. Sommariva, D. Carlson, H. Wang, J. West, and Z. Liu. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research. (Submitted) preprint

  • Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue

Qiu, M., J. Li, C.F. Gould, R. Jing, M. Kelp, M.L. Childs, J. Wen, Y. Xie, M. Lin, M.V. Kiang, S. Heft-Neal, N.S. Diffenbaugh, M. Burke. Wildfire smoke exposure and mortality burden in the US under future climate change. (In review) preprint

Publications (advisee)

2025

21. Kelp, M., M. Burke, M. Qiu, I. Higuera-Mendieta, T. Liu, and N. Diffenbaugh (2025). Effect of Recent Prescribed Burning and Land Management on Wildfire Burn Severity and Smoke Emissions in the Western United States, AGU Advances (In press) preprint


20. Chung, K., T. Liu, M. Kelp, K. Vohra, D. Skelly, M. Carroll, J. Schwartz, and L. Mickley (2025). Managing Smoke Risk from Wildland Fires: Northern California as a Case Study, Environ. Sci. Technol. (In press) preprint, video demo


19. Qiu, M., D. Chen, M. Kelp, J. Li, G. Huang, M.D. Yazdi (2025). The rising threats of wildland-urban interface fires in the era of climate change: The Los Angeles 2025 fires, The Innovation, DOI: 10.1016/j.xinn.2025.100835.


18. Kawano, A., M. Kelp, M. Qiu, K. Singh, E. Chaturvedi, I. Azevedo, and M. Burke (2025). Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality, Science Advances, 11, 4, DOI: sciadv.adq1071.

2024

17. Qiu, M., M. Kelp, S. Heft-Neal, X. Jin, C.F. Gould, D.Q. Tong, and M. Burke (2024). Evaluating Estimation Methods for Wildfire Smoke and their Implications for Assessing Health Effects, Environ. Sci. Technol., 58, 52, 22880–22893, DOI: 10.1021/acs.est.4c05922

  • Special Issue on “Wildland Fires: Emissions, Chemistry, Contamination, Climate, and Human Health”


16. Liu, T., F.M. Panday, M.C. Caine, M. Kelp, D.C. Pendergrass, and L.J. Mickley (2024). Is the smoke aloft? Caveats regarding the use of the Hazard Mapping System (HMS) smoke product as a proxy for surface smoke presence across the United States, International Journal of Wildland Fire, 33, WF23148, DOI: 10.1071/WF23148.


15. Lin, H., L.K. Emmons, E.W. Lundgren, L.H. Yang, X. Feng, R. Dang, S. Zhai, Y. Tang, M. Kelp, N.K. Colombi, S.D. Eastham, T.M. Fritz, A.M. Fiore, and D.J. Jacob (2024). Intercomparison of GEOS-Chem and CAM-chem tropospheric oxidant chemistry within the Community Earth System Model version 2 (CESM2), Atmospheric Chemistry and Physics, 24, 8607–8624, DOI: 10.5194/acp-24-8607-2024.

2023

14. Kelp, M., C. A. Keller, K. Wargan, B.M. Karpowicz, and D. J. Jacob (2023). Tropospheric ozone data assimilation in the NASA GEOS Composition Forecast modeling system (GEOS-CF v2.0) using satellite data for ozone vertical profiles (MLS), total ozone columns (OMI), and thermal infrared radiances (AIRS, IASI), Environ. Res. Lett., 18, 094036, DOI: 10.1088/1748-9326/acf0b7.


13. Kelp, M., T. Fargiano, S. Lin, T. Liu, J.R. Turner, J. N. Kutz, and L.J. Mickley (2023). Data-driven placement of PM2.5 air quality sensors in the United States: an approach to target urban environmental injustice, GeoHealth, 7, e2023GH000834, DOI: 10.1029/2023GH000834.

  • Special Collection on “Geospatial data applications for environmental justice”


12. Balasus, N., D. J. Jacob, A. Lorente, J. D. Maasakkers, R. J. Parker, H. Boesch, Z. Chen, M., Kelp, H. Nesser, and D. J. Varon (2023). A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases, Atmos. Meas. Tech., 16, 3787–3807, DOI: 10.5194/amt-16-3787-2023.


11. Kelp, M., M. Carroll, T. Liu, R. M. Yantosca, H.E. Hockenberry, and L.J. Mickley (2023). Prescribed burns as a tool to mitigate future wildfire smoke exposures: Lessons for states and environmental justice communities, Earth’s Future, 11, e2022EF003468, DOI: 10.1029/2022EF003468.

  • Among Earth’s Future Top Viewed Articles of 2023

2022

10. Kelp, M., D.J. Jacob, H. Lin, and M.P. Sulprizio (2022). An online-learned neural network chemical solver for stable long-term global simulations of atmospheric chemistry, JAMES, 14, e2021MS002926, DOI: 10.1029/2021MS002926.


9. Yang, L. H., D.H. Hagan, J.C. Rivera-Rios, M. Kelp, E.S. Cross, C.Y. Peng, J. Kaiser, L.R. Williams, P. L. Croteau, J.T. Jayne, and N.L. Ng (2022). Investigating the sources of urban air pollution using low-cost air quality sensors at an urban Atlanta site, Environ. Sci. Technol., 56, 11, 7063–7073, DOI: 10.1021/acs.est.1c07005.

  • Special Issue on “Urban Air Pollution and Human Health”


8. Kelp, M., S. Lin, J.N. Kutz, and L.J. Mickley (2022). A new approach for optimal placement of PM2.5 air quality sensors: case study for the contiguous United States, Env. Res. Letters, 17, 034034, DOI: 10.1088/1748-9326/ac548f.

2020

7. Kelp, M., D.J. Jacob, J.N. Kutz, J.D. Marshall, and C. Tessum (2020). Toward stable, general machine-learned models of the atmospheric chemical system, JGR: Atmospheres, 125, e2020JD032759, DOI: 10.1029/2020JD032759.


6. Kelp, M., T. Gould, E. Austin, J.D. Marshall, M. Yost, C. Simpson, and T. Larson (2020). Sensitivity analysis of area-wide, mobile source emission factors to high-emitter vehicles in Los Angeles, Atmospheric Environment, 223, 117212, DOI: 10.1016/j.atmosenv.2019.117212.

2019

5. Wen, Y., H. Wang, T. Larson, M. Kelp, S. Zhang, Y. Wu, and J.D. Marshall (2019). On-highway vehicle emission factors, and spatial patterns, based on mobile monitoring and absolute principal component score, Science of The Total Environment, 676, 242-251, DOI: 10.1016/j.scitotenv.2019.04.185.

2018

4. Kelp, M., C. Tessum, and J.D. Marshall (2018). Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation, arXiv: 1808.03874.


3. Kelp, M., A.P. Grieshop, C.O. Reynolds, J. Baumgartner, G. Jain, K. Sethuramanand, and J.D. Marshall (2018). Real-time indoor measurement of health and climate-relevant air pollution concentrations during a carbon-finance-approved cookstove intervention in rural India, Development Engineering, 3, 125-132, DOI: 10.1016/j.deveng.2018.05.001.

2017

2. Brewer, J. F., M. Bishop, M. Kelp, C. Keller, A.R. Ravishankara, and E.V. Fischer (2017). A sensitivity analysis of key factors in the modeled global acetone budget, J. Geophys. Res., 122, DOI: 10.1002/2016JD025935.

2015

1. Jaffe, D., J. Putz, G. Hof, G. Hof, J. Hee, D.A. Lommers-Johnson, F. Gabela, J. Fry, B. Ayres, M. Kelp, and M. Minsk (2015). Diesel particulate matter and coal dust from trains in the Columbia River Gorge, Washington state, USA, Atmospheric Pollution Research, 6, 946-952, DOI: 10.1016/j.apr.2015.04.004.

Other Publications

2. Kelp, M., 2023. “Expanding the Capabilities of Atmospheric Chemistry Models and Datasets Using Machine Learning and Data-Driven Methods”, Harvard University dissertation

1. Kelp, M., 2016. “Tropospheric particle formation in forests: global modeling of secondary organic aerosol production from reaction of NO3 radical with speciated monoterpenes”,
Reed College chemistry thesis