h-index: 7, citations: 131 (as of Sep 2022, Google Scholar); as 1st author (6), as co-author (4)

Publications In-Prep, Submitted, and Under Review

Kelp, M., T. Liu, and L.J. Mickley. Sensitivity of population smoke exposure to wildfires in the Western United States: implications of prescribed burning for states and rural environmental justice communities, (In-prep)

Kelp, M., T. Fargiano, S. Lin, T. Liu, J. N. Kutz, and L.J. Mickley. Data-driven placement of PM2.5 air quality sensors in the United States: an approach to target urban environmental injustice, (In-prep)

Kelp, M., C. A. Keller, K. Wargan, B.M. Karpowicz, and D. J. Jacob. Tropospheric ozone data assimilation in the NASA GEOS Composition Forecast Modeling System GEOS-CF v2.0 including direct assimilation of thermal infra-red radiances, (In-prep)

Peer-Reviewed Publications


9. 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
*Editor’s Highlight in JAMES, Special Collection on “Machine learning application to Earth system modeling”

8. 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”

7. 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
** undergraduate advisee


6. 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

5. 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


4. 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


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

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


Links to abstracts and posters