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Sina Hasheminassab

Government of the United States of America - Jet Propulsion Laboratory

Pasadena, CA

United States

  • Government of the United States of America - Jet Propulsion Laboratory

SCHOLARLY PAPERS

3

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98

TOTAL CITATIONS

0

Scholarly Papers (3)

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1.

Spatial modeling of ambient PM₂.₅ and population exposure in Addis Ababa using land use regression

Number of pages: 46 Posted: 16 Apr 2026
Desalew Meseret Moges, Per-Ola Olsson, Ebba Malmqvist, Christina Isaxon, Muluneh Getachew Garedew, Araya Asfaw, Sina Hasheminassab, Tesfaye Mamo, Kedir Teji Roba, Gudina Terefe Tucho, Masresha Tessema, Eleni Papadopoulou and Kristoffer Mattisson
Lund University, Lund University, Lund University - Division of Occupational and Environmental Medicine, Lund University, Lund University, Addis Ababa University, Government of the United States of America - Jet Propulsion Laboratory, Addis Ababa University, Haramaya University - College Health and Medical Science, affiliation not provided to SSRN, Ethiopian Public Health Institute, Norwegian Institute of Public Health and Lund University
Downloads 39 (1,236,159)

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ambient air pollution, Land use, Particulate matter, urban air quality, Health risk assessment, Ethiopia

2.

Long-Term Source Apportionment of PM 2.5 Across the Contiguous United States (2000-2019) Using a Multilinear Engine Model

Number of pages: 21 Posted: 28 Feb 2024
Qiao Zhu, Yang Liu and Sina Hasheminassab
Emory University, Emory University and Government of the United States of America - Jet Propulsion Laboratory
Downloads 37 (1,222,651)

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PM2.5 source apportionment, Emission reductions, Fossil fuel emissions, Air quality policies

3.

Prediction of Ambient Pm2.5 Chemical Components in Southern California Using Machine Learning

Number of pages: 47 Posted: 16 Jul 2025
Jiani Yang, Sina Hasheminassab, Meredith Franklin, Antong Zhang, David Diner, Joseph Pinto and Yuk Yung
California Institute of Technology (Caltech), Government of the United States of America - Jet Propulsion Laboratory, University of Toronto, Brown University, Government of the United States of America - Jet Propulsion Laboratory, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 22 (1,424,331)

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PM2.5 chemical composition, Machine Learning, XGBoost, Air pollution prediction, Southern California, Atmospheric aerosols, Speciation monitoring, SHAP analysis, Meteorological predictors, Air quality modeling