h-index: 8 (as of April 2023, Google Scholar); as 1st author (7), as co-author (4)
Publications In-Prep, Submitted, and Under Review
Kelp, M., T. Fargiano, S. Lin, T. Liu, J.R. Turner, 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 review at GeoHealth, preprint)
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) using satellite data for ozone vertical profiles (MLS), total ozone columns (OMI), and thermal infrared radiances (AIRS, IASI), (Submitted to ERL)
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. A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases, (Submitted to Atmos. Meas. Tech.)
Peer-Reviewed Publications
2023
10. 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. Accepted at Earth’s Future
2022
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”, Royal Meteorological Society Atmospheric Chemistry Special Interest Conference Talk
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
2020
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
2019
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
2018
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., 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