- h-index: 11 (Google Scholar)
- Selected recorded seminars, general talks, and press can be found on my Media page
** undergraduate advisee
Submitted and Under Review
Kelp, M., M. Burke, M. Qiu, I. Higuera-Mendieta, T. Liu, and N. Diffenbaugh. Efficacy of Recent Prescribed Burning and Land Management on Wildfire Burn Severity and Smoke Emissions in the Western United States. (In review at PNAS)
Qiu, M., M. Kelp, S. Heft-Neal, X. Jin, C.F. Gould, D.Q. Tong, and M. Burke. Evaluating Estimation Methods for Wildfire Smoke and their Implications for Assessing Health Effects. (In review at Environ. Sci. Technol.) preprint
Kawano, A., M. Kelp, M. Qiu, K. Singh, E. Chaturvedi, I. Azevedo, and M. Burke. Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality. (In review at Science Advances) preprint
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 at Science)
Publications
2024
16.
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. 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. 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
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
- Special Collection on “Machine learning application to Earth system modeling”
- Selected as Highlight Paper
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