• h-index: 9 (as of September 2023, Google Scholar); as 1st author (9), as co-author (5)
  • Selected recorded seminars, general talks, and press can be found on my Media page

** undergraduate advisee

Submitted and Under Review

Liu, T., F.M. Panday**, M.C. Caine**, M. Kelp, D.C. Pendergrass, and L.J. Mickley. 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. (Submitted to International Journal of Wildland Fire)



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


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


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


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


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


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


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