Atmospheric Composition Modeling and Analysis Program

Kacenelenbogen, M.S.F., et al. (2022), Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition?, Atmos. Chem. Phys., doi:10.5194/acp-22-3713-2022.

Wang, M., et al. (2022), Synergistic HNO3–H2SO4–NH3 upper tropospheric particle formation, Nature, doi:10.1038/s41586-022-04605-4.

Jayakumar, A., et al. (2022), Delhi Model with Chemistry and aerosol framework (DM-Chem) for high-resolution fog forecasting, Q. J. R. Meteorol. Soc., 147, 3957-3978, doi:10.1002/qj.4163.

Li, C., and R.C. Cohen (2021), Space-borne estimation of volcanic sulfate aerosol lifetime, J. Geophys. Res., 126, org/10.1029/2020JD033883.

Lawson, R.P., et al. (2022), Coalescence and Secondary Ice Development in Cumulus Congestus Clouds, J. Atmos. Sci., 79, 953-972, doi:10.1175/JAS-D-21-0188.1.

Zhao, B., et al. (2022), High concentration of ultrafine particles in the Amazon free troposphere produced by organic new particle formation, Proc. Natl. Acad. Sci., 117-25344, doi:10.1073/pnas.2006716117.

Jin, X., et al. (2021), Direct estimates of biomass burning NOx emissions and lifetimes using daily observations from TROPOMI, Atmos. Chem. Phys., 21, 15569-15587, doi:10.5194/acp-21-15569-2021.

Zhu, Q., et al. (2022), RESEARCH ARTICLE EARTH, ATMOSPHERIC, AND PLANETARY SCIENCES OPEN ACCESS Estimate of OH trends over one decade in North American cities, Proc. Natl. Acad. Sci., doi:10.1073/pnas.2117399119.

Zhu, Q., et al. (2022), Combining machine learning and satellite observations to predict spatial and temporal variation of surface OH in cities, Env. Sci and Tech., 56, 7362-7371, doi:10.1021/acs.est.1c05636.