Publications

Group members are listed in bold.

Submitted & Under Review

  • Silva, S. J. and Evans, M. (Submitted) Artificial Intelligence and Machine Learning in Atmospheric Chemistry, Editorial for ACS ES&T Air

  • Silva, S. J. and Halappanavar M. (Submitted) Graph Characterization of Higher Order Structure in Atmospheric Chemical Reaction Mechanisms [Preprint]

  • Lyman, K., Krishnamoorthy, B., Silva, S. J., Halappanavar, M., Kalyanaraman, A.,Keller, C., and Barber, V. (Submitted) Persistent Cycles in Dynamic Directed Bipartite Graphs: An Application in Atmospheric Chemistry

  • John, S. G., Pasquier, B, Holzer, M, Silva, S. J., (Under Review) Biogeochemical fluxes of nickel in the global oceans inferred from a diagnostic model [Preprint]

Peer Reviewed

2024

  • Azzouz, M, Hasan, Z, Rahman, Md. M., Gauderman, W. J., Lorenzo, M, Lurmann, F. W., Eckel, S. P., Palinkas, L., Johnston, J., Hurlburt, M., Silva, S. J., Schlaerth, H., Ko, J, Ban-Weiss, G, McConnell, R, Stockfelt, L, Garcia, E. (Accepted, 2024) Does socioeconomic and environmental burden affect vulnerability to extreme air pollution and heat? - A case-crossover study of mortality in California. JESSE

  • Silva, S. J., and Keller, C. A. (2024) Limitations of XAI methods for process-level understanding in the atmospheric sciences, AIES 3, no. 1 (January 2024): e230045. [AIES]

  • Yu, S., Ma, P.-L., Singh, B., Silva, S. J., Pritchard, M. (2024) Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset, AIES 3, no. 1 (January 2024): e230013. [AIES]

  • Ziming, L., Sturm, P. O., Bharadwaj, S., Silva, S. J., and Tegmark, M. (2024) Discovering New Interpretable Conservation Laws as Sparse Invariants. Phys. Rev. E [PRE]

  • Peplinski, M., Dilkina, B., Silva, S. J., Ban-Weiss, G., Sanders, K. T. (2024). A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets, Applied Energy, 2024, [Applied Energy]

2023

  • Schlaerth, H. L., Silva, S. J., Li, Y., Li, D. (2023) Albedo as a competing warming effect of urban greening, JGR: Atmospheres, 128, e2023JD038764. [JGRA]

  • Schlaerth, H. L., Silva, S. J., Li, Y. (2023) Characterizing ozone sensitivity to urban greening in Los Angeles under current day and future anthropogenic emissions scenarios, JGR: Atmospheres, September 11, 2023, e2023JD039199. [JGRA]

  • Clifton, O. E., Schwede, D., Hogrefe, C., Bash, J. O., Bland, S., Cheung, P., Coyle, M., Emberson, L., Flemming, J., Fredj, E., Galmarini, S., Ganzeveld, L., Gazetas, O., Goded, I., Holmes, C., D., Horváth, L., Huijnen, V., Li, Q., Makar, P. A., Mammarella, I., Manca, G., Munger, J. W., Pérez-Camanyo, J. L., Pleim, J., Ran, L., San Jose, R., Silva, S. J., Staebler, R., Sun, S., Tai, A. P. K., Tas, E., Vesala, T., Weidinger, T., Wu, Z., and Zhang, L. (Accepted, 2023) A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4), Atmos. Chem. Phys., 23, 9911–9961, 2023. [ACP]

  • Yik, W., Silva, S. J., Geiss, A., Watson-Parris, D. (2023). Exploring Randomly Wired Neural Networks for Climate Model Emulation. AIES, no. 4 (October 2023): 220088. [AIES]

  • Palinkas, L. A., De Leon, J., Yu, K., Salinas, E., Fernandez, C., Johnston, J, Rahman, M., Md., Silva, S. J., Hurlburt, M., McConnell, R. S., Garcia, E., (2023) Adaptation resources and responses to wildfire smoke and other forms of air pollution in low-income urban settings: A mixed-methods study. IJERPH, 20, no. 7 (April 4, 2023): 5393. [IJERPH]

  • Silva, S. J., Burrows, S. M., Calvin, K., Cameron-Smith, P. J., Shi, X., Zhou, T. (2023). Contrasting the biophysical and radiative effects of rising CO2 concentrations on ozone dry deposition fluxes. JGR: Atmospheres, 128, no. 6 (March 27, 2023): e2022JD037668. https://doi.org/10.1029/2022JD037668.

  • Rahman, Md Mostafijur, Lorenzo, M, Ban-Weiss, G, Hasan, Z, Azzouz, M, Eckel, S. P., Conti, D. V., Lurmann, F. W., Schlaerth, H, Johnston, J, Ko, J, Palinkas, L, Hurlburt, M, Silva, S. J., W Gauderman, W. J., McConnell, R, and Garcia, E., (2023) Ambient temperature and air pollution associations with suicide and homicide mortality in California: A Statewide Case-Crossover Study. STOTEN , 874 (May 2023): 162462. [STOTEN]

2022

  • Palinkas, L. A., Hurlburt, M. S., Fernandez, C., De Leon, J., Yu, K., Salinas, E., Garcia, E. Johnston, J., Rahman, M. M., Silva, S. J., McConnell, R. S. (2022). Adaptation Resources and Behaviors to Heat Waves of Low-income Residents of Urban Heat Islands: A Qualitative Study. IJERPH https://doi.org/10.3390/ijerph191711090 [IJERPH]

  • Geiss, A., Silva, S. J. and Hardin, J. C. (2022) Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning. Geosci. Model Dev.  https://doi.org/10.5194/gmd-15-6677-2022 [GMD]

  • Rahman, Md Mostafijur, McConnell, R., Schlaerth, H., Ko, J., Silva, S. J., Lurmann, F. W., Palinkas, L., Johnston, J., Hurlburt, M., Yin, H., Ban-Weiss, G and Garcia, E. (2022) “The Effects of Co-Exposure to Extremes of Heat and Particulate Air Pollution on Mortality in California: Implications for Climate Change.” American Journal of Respiratory and Critical Care Medicine, June 21, 2022, rccm.202204-0657OC. https://doi.org/10.1164/rccm.202204-0657OC [AJRCCM]

  • Silva, S. J., Keller, C. A, & Hardin, J. (2022). Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence. Journal of Advances in Modeling Earth Systems, 14, e2021MS002881. https://doi.org/10.1029/2021MS002881 [JAMES]

2021

  • Galmarini, S., Makar, P., Clifton, O. E., Hogrefe, C., Bash, J. O., Bellasio, R., Bianconi, R., Bieser, J., Butler, T., Ducker, J., Flemming, J., Hodzic, A., Holmes, C. D., Kioutsioukis, I., Kranenburg, R., Lupascu, A., Perez-Camanyo, J. L., Pleim, J., Ryu, Y.-H., San Jose, R., Schwede, D., Silva, S. J., and Wolke, R. (2021) Technical note: AQMEII4 Activity 1: evaluation of wet and dry deposition schemes as an integral part of regional-scale air quality models. Atmos. Chem. Phys., 21, 15663–15697, https://doi.org/10.5194/acp-21-15663-2021 [ACP]

  • Silva, S. J., Ma P.-L., Hardin J. C., and Rothenberg D. (2021) Physically Regularized Machine Learning Emulators of Aerosol Activation. Geosci. Model Dev. 14, no. 5 (May 28, 2021): 3067–77. https://doi.org/10.5194/gmd-14-3067-2021 [GMD]

  • Silva, S. J., Burrows, S. M., Evans, M. J., and Halappanavar, M. (2021). A Graph Theoretical Intercomparison of Atmospheric Chemical Mechanisms. Geophys. Res. Lett., 48, e2020GL090481. https://doi.org/10.1029/2020GL090481 [GRL]

2020

  • Silva, S. J., Ridley, D. A., and Heald, C. L. (2020). Exploring the constraints on simulated aerosol sources and transport across the North Atlantic with island-based sun photometers. Earth and Space Science, 7, e2020EA001392. https://doi.org/10.1029/2020EA001392 [ESS]

  • Silva, S. J., Heald, C. L., and Guenther, A. B. (2020) Development of a Reduced Complexity Plant Canopy Physics Surrogate Model for use in Chemical Transport Models: A Case Study with GEOS-Chem v12.3.0. Geosci. Model Dev. 13, no. 6 (June 3, 2020): 2569–85. https://doi.org/10.5194/gmd-13-2569-2020 [GMD]

  • Clifton, O. E., Fiore, A. M. , Massman, W. J., Baublitz, C. B., Coyle, M., Emberson, L., Fares, S., Farmer, D. K., Gentine, P., Gerosa, G., Guenther, A. B., Helmig, D., Lombardozzi, D. L., Munger, J. W., Patton, E. G., Pusede, S. E., Schwede, D. B., Silva, S. J., Sörgel, M., Steiner, A. L., and Tai, A. P. K., (2020) Dry deposition of ozone over land: processes, measurements and modeling. Reviews of Geophysics. https://doi.org/10.1029/2019RG000670 [RoG]

2019

  • Wong, A. Y. H., Geddes, J. A., Tai, A. P. K., and Silva, S. J. (2019). Importance of Dry Deposition Parameterization Choice in Global Simulations of Surface Ozone. Atmos. Chem. Phys., 19, no. 22: 14365–85. https://doi.org/10.5194/acp-19-14365-2019 [ACP]

  • Silva, S. J., Heald, C. L., Ravela, S., Mammarella, I., and Munger, J.W. (2019). A Deep Learning Parameterization for Ozone Dry Deposition Velocities. Geophys. Res. Lett., 46. https://doi.org/ 10.1029/2018GL081049 [GRL]

2018

  • Silva, S. J., Heald, C. L., and Li, M. (2018). Space-Based Constraints on Terrestrial Glyoxal Production. JGR: Atmospheres, 123(23), 13,583-13,594. doi.org/10.1029/2018JD029311 [JGR]

  • Silva, S. J., Barbieri, L. K., and Thomer, A. K.  (2018). Observing Vegetation Phenology through Social Media. PLOS ONE 13, no. 5 (May 10, 2018): e0197325. doi:10.1371/journal.pone.0197325. [PONE]

  • Silva, S. J., and Heald, C. L. (2018). Investigating dry deposition of ozone to vegetation. JGR: Atmospheres, 123, 559–573. doi:10.1002/2017JD027278 [JGR]

2017

  • Silva, S. J. and Arellano, A. F. (2017). Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2. Remote Sens. 2017, 9, 744, [RS]

2016 & Earlier

  • Silva, S.J., Heald, C. L., Geddes, J. A., Austin, K. G., Kasibhatla, P. S., and Marlier, M. E. (2016). Impacts of Current and Projected Oil Palm Plantation Expansion on Air Quality Over Southeast Asia, Atmos. Chem. Phys., 16, 10621-10635, doi:10.5194/acp-16-10621-2016 [ACP]

  • Geddes, J. A., Heald, C. L., Silva, S. J., and Martin, R. V. (2016). Land cover change impacts on atmospheric chemistry: simulating projected large-scale tree mortality in the United States, Atmos. Chem. Phys., 16, 2323-2340, doi:10.5194/acp-16-2323-2016, 2016. [ACP]​

  • Silva, S. J., Arellano, A.F., and Worden, H. (2013). Toward anthropogenic combustion emission constraints from space-based analysis of urban CO2/CO sensitivity, Geophys. Res. Lett., 40, doi:10.1002/grl.50954 [GRL]