Sam J. Silva

1.3k total citations
39 papers, 570 citations indexed

About

Sam J. Silva is a scholar working on Atmospheric Science, Global and Planetary Change and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Sam J. Silva has authored 39 papers receiving a total of 570 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Atmospheric Science, 19 papers in Global and Planetary Change and 12 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Sam J. Silva's work include Atmospheric chemistry and aerosols (16 papers), Air Quality and Health Impacts (12 papers) and Climate Change and Health Impacts (8 papers). Sam J. Silva is often cited by papers focused on Atmospheric chemistry and aerosols (16 papers), Air Quality and Health Impacts (12 papers) and Climate Change and Health Impacts (8 papers). Sam J. Silva collaborates with scholars based in United States, United Kingdom and Canada. Sam J. Silva's co-authors include Colette L. Heald, Avelino F. Arellano, Joseph Hardin, Christoph A. Keller, H. M. Worden, Jeffrey A. Geddes, Rob McConnell, Lawrence A. Palinkas, Erika Garcia and Michael S. Hurlburt and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Science of The Total Environment.

In The Last Decade

Sam J. Silva

35 papers receiving 560 citations

Peers

Sam J. Silva

GPC

HTM

Noelia Otero Germany

Alaa Mhawish China

Christos Giannaros Greece

Boming Liu China

Do‐Woo Kim South Korea

U. Kaminski Germany

Kostas Eleftheratos Greece

D. B. Jadhav India

Han Han China

Hakki Baltaci Türkiye

Noelia Otero Germany

Sam J. Silva

230 ×0.8

161 ×0.6

GPC

161 ×0.7

HTM

93 ×0.8

17 ×0.3

Citations per year, relative to Sam J. Silva Sam J. Silva (= 1×) peers Noelia Otero

Countries citing papers authored by Sam J. Silva

Since Specialization

Citations

This map shows the geographic impact of Sam J. Silva's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sam J. Silva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam J. Silva more than expected).

Fields of papers citing papers by Sam J. Silva

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sam J. Silva. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sam J. Silva. The network helps show where Sam J. Silva may publish in the future.

Co-authorship network of co-authors of Sam J. Silva

This figure shows the co-authorship network connecting the top 25 collaborators of Sam J. Silva. A scholar is included among the top collaborators of Sam J. Silva based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sam J. Silva. Sam J. Silva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Garcia, Erika, et al.. (2025). Efficiency of case-crossover versus time-series study designs for extreme heat exposures. Environmental Epidemiology. 9(2). e370–e370.

Silva, Sam J. & M. J. Evans. (2024). Artificial Intelligence and Machine Learning in Atmospheric Chemistry. ACS ES&T Air. 1(5). 330–331. 1 indexed citations

Rahman, Md Mostafijur, W. James Gauderman, Frederick Lurmann, et al.. (2024). Does socioeconomic and environmental burden affect vulnerability to extreme air pollution and heat? A case-crossover study of mortality in California. Journal of Exposure Science & Environmental Epidemiology. 35(2). 294–302. 8 indexed citations

Silva, Sam J., et al.. (2024). A Nudge to the Truth: Atom Conservation as a Hard Constraint in Models of Atmospheric Composition Using a Species-Weighted Correction. ACS ES&T Air. 2(1). 99–108. 2 indexed citations

John, Seth G., et al.. (2024). Biogeochemical Fluxes of Nickel in the Global Oceans Inferred From a Diagnostic Model. Global Biogeochemical Cycles. 38(5). 4 indexed citations

Dilkina, Bistra, et al.. (2023). A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets. Applied Energy. 357. 122413–122413. 14 indexed citations

Palinkas, Lawrence A., Kexin Yu, Jill Johnston, et al.. (2023). Adaptation Resources and Responses to Wildfire Smoke and Other Forms of Air Pollution in Low-Income Urban Settings: A Mixed-Methods Study. International Journal of Environmental Research and Public Health. 20(7). 5393–5393. 3 indexed citations

Silva, Sam J., et al.. (2023). Characterizing Ozone Sensitivity to Urban Greening in Los Angeles Under Current Day and Future Anthropogenic Emissions Scenarios. Journal of Geophysical Research Atmospheres. 128(20). 5 indexed citations

Silva, Sam J., et al.. (2023). Exploring Randomly Wired Neural Networks for Climate Model Emulation. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2(4). 2 indexed citations

10.

Yu, Sungduk, et al.. (2023). Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3(1).

11.

Silva, Sam J., et al.. (2022). Downscaling atmospheric chemistry simulations with physically consistent deep learning. Geoscientific model development. 15(17). 6677–6694. 14 indexed citations

12.

Rahman, Md Mostafijur, Rob McConnell, Sam J. Silva, et al.. (2022). The Effects of Coexposure to Extremes of Heat and Particulate Air Pollution on Mortality in California: Implications for Climate Change. American Journal of Respiratory and Critical Care Medicine. 206(9). 1117–1127. 93 indexed citations

13.

Silva, Sam J., Po‐Lun Ma, Joseph Hardin, & Daniel Rothenberg. (2021). Physically regularized machine learning emulators of aerosol activation. Geoscientific model development. 14(5). 3067–3077. 18 indexed citations

14.

Silva, Sam J., Susannah M. Burrows, M. J. Evans, & Mahantesh Halappanavar. (2020). A Graph Theoretical Intercomparison of Atmospheric Chemical Mechanisms. Geophysical Research Letters. 48(1). 15 indexed citations

15.

Silva, Sam J., Po‐Lun Ma, Joseph Hardin, & Daniel Rothenberg. (2020). Physically Regularized Machine Learning Emulators of AerosolActivation. 1 indexed citations

16.

Silva, Sam J., Colette L. Heald, & Alex Guenther. (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. Geoscientific model development. 13(6). 2569–2585. 7 indexed citations

17.

Geddes, Jeffrey A., et al.. (2019). Importance of dry deposition parameterization choice in global simulations of surface ozone. Atmospheric chemistry and physics. 19(22). 14365–14385. 23 indexed citations

18.

Silva, Sam J., Lindsay Barbieri, & Andrea K. Thomer. (2018). Observing vegetation phenology through social media. PLoS ONE. 13(5). e0197325–e0197325. 15 indexed citations

19.

Silva, Sam J., Colette L. Heald, Jeffrey A. Geddes, et al.. (2016). Impacts of current and projected oil palm plantation expansion on airquality over Southeast Asia. Atmospheric chemistry and physics. 16(16). 10621–10635. 12 indexed citations

20.

Geddes, Jeffrey A., Colette L. Heald, Sam J. Silva, & Randall V. Martin. (2016). Land cover change impacts on atmospheric chemistry: simulating projected large-scale tree mortality in the United States. Atmospheric chemistry and physics. 16(4). 2323–2340. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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