Publications
Group members/mentees are listed in bold.
Submitted & Under Review
Silva, S. J. and Halappanavar M. (Under Review) 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
Peer Reviewed
2024
John, S. G., Pasquier, B, Holzer, M, Silva, S. J., (Accepted, 2024) Biogeochemical fluxes of nickel in the global oceans inferred from a diagnostic model. GBC [Preprint]
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. (2024) Does socioeconomic and environmental burden affect vulnerability to extreme air pollution and heat? - A case-crossover study of mortality in California. JESSE, May 7, 2024. [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. (2023) A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4) [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. [JGRA]
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., Vivanco M. G., 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]
Other Publications
Silva, S. J. and Evans, M. (2024) Artificial Intelligence and Machine Learning in Atmospheric Chemistry, ACS ES&T Air, 1, no. 5 (May 10, 2024): 330–31. Editorial for ACS ES&T Air