I am a professor of urban systems engineering and director of NYU’s cross-disciplinary PhD program in Urban Systems. My research focus is on climate resilience of infrastructure systems, using methods that include sensing, data acquisition and modeling. This work is aimed at the fundamental understanding of the health and performance of the built environment, and the critical interdependencies across human, natural, and physical systems. The foundations of my research were in the development of methods for the quantitative assessment of materials aging, at both micro and meso-scale. That work has evolved to domains in the macro-scale, where I study the life cycle dynamics of the built environment; its sustainability and resilience, including service delivery and human impacts when exposed to extreme conditions. I like to work with multidisciplinary research teams, developing taxonomies to define systems interdependencies, and producing data platforms to address complex phenomena for planning and governance.
Education
Columbia University
Bachelor of Science, Civil Engineering & Applied Mechanics
McGill University
Master of Science, Civil Engineering
Northwestern University
Doctor of Philosophy, Civil and Environmental Engineering
Publications
Past 10 years
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Azad, S.; Ferrer-Cid, P.; Ghandehari, M. Exposure to Fine Particulate Matter in the New York City Subway System during Home-Work Commute. PLOS ONE 2024, 19, e0307096, doi:10.1371/journal.pone.0307096.
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Fitzky, M.; Brozovsky, J.; Ghandehari, M. Simulation of Physiological Equivalent Temperature in an Urban Microclimate. 2024, doi:10.2139/ssrn.4972652.
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Montoya-Rincon, J.P.; Mejia-Manrique, S.A.; Azad, S.; Ghandehari, M.; Harmsen, E.W.; Khanbilvardi, R.; Gonzalez-Cruz, J.E. A Socio-Technical Approach for the Assessment of Critical Infrastructure System Vulnerability in Extreme Weather Events. Nat. Energy 2023, 8, 1002–1012, doi:10.1038/s41560-023-01315-7.
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Azad, S.; Luglio, D.G.; Gordon, T.; Thurston, G.; Ghandehari, M. Particulate Matter Concentration and Composition in the New York City Subway System. Atmos. Pollut. Res. 2023, 14, 101767, doi:10.1016/j.apr.2023.101767.
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Rinaldi, V.; Motoa, G.; Ghandehari, M. Trees as Sensors, Distribution of Wind Intensity During Hurricane Maria. IGARSS 2023 - 2023 IEEE Int. Geosci. Remote Sens. Symp. 2023, 00, 2422–2425, doi:10.1109/igarss52108.2023.10281466.
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Azad, S.; Ghandehari, M. Emissions of Nitrogen Dioxide in the Northeast U.S. during the 2020 COVID-19 Lockdown. J. Environ. Manag. 2022, 312, 114902, doi:10.1016/j.jenvman.2022.114902.
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Carvalhaes, T.; Rinaldi, V.; Goh, Z.; Azad, S.; Uribe, J.; Ghandehari, M. Integrating Spatial and Ethnographic Methods for Resilience Research: A Thick Mapping Approach for Hurricane Maria in Puerto Rico. Ann. Am. Assoc. Geogr. 2022, 112, 2413–2435, doi:10.1080/24694452.2022.2071200.
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Montoya-Rincon, J.P.; Azad, S.; Pokhrel, R.; Ghandehari, M.; Jensen, M.P.; Gonzalez, J.E. On the Use of Satellite Nightlights for Power Outages Prediction. IEEE Access 2022, 10, 16729–16739, doi:10.1109/access.2022.3149485.
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Azad, S.; Ghandehari, M. A Study on the Association of Socioeconomic and Physical Cofactors Contributing to Power Restoration After Hurricane Maria. IEEE Access 2021, 9, 98654–98664, doi:10.1109/access.2021.3093547.
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Carvalhaes, T.; Rinaldi, V.; Goh, Z.; Azad, S.; Uribe, J.; Chester, A.; Ghandehari, M. Integrating Spatial and Ethnographic Methods for Resilience Research: A Thick Mapping Approach for Hurricane Maria in Puerto Rico. SSRN Electron. J. 2021, doi:10.2139/ssrn.3863657.
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Dobler, G.; Bianco, F.B.; Sharma, M.S.; Karpf, A.; Baur, J.; Ghandehari, M.; Wurtele, J.; Koonin, S.E. The Urban Observatory: A Multi-Modal Imaging Platform for the Study of Dynamics in Complex Urban Systems. Remote Sens. 2021, 13, 1426, doi:10.3390/rs13081426.
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Azad, S.; Ghandehari, M. Trends in the Reduction of Nitrogen Dioxide Emissions: Effect of 2020 SARS-CoV-2 Pandemic Versus Emission Reduction Regulations. SSRN Electron. J. 2021, doi:10.2139/ssrn.3891885.
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González, J.E.; Ramamurthy, P.; Bornstein, R.D.; Chen, F.; Bou-Zeid, E.R.; Ghandehari, M.; Luvall, J.; Mitra, C.; Niyogi, D. Urban Climate and Resiliency: A Synthesis Report of State of the Art and Future Research Directions. Urban Clim. 2021, 38, 100858, doi:10.1016/j.uclim.2021.100858.
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Caplin, A.; Ghandehari, M.; Lim, C.; Glimcher, P.; Thurston, G. Advancing Environmental Exposure Assessment Science to Benefit Society. Nat. Commun. 2019, 10, 1236, doi:10.1038/s41467-019-09155-4.
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Karimi, M.; Nazari, R.; Dutova, D.; Khanbilvardi, R.; Ghandehari, M. A Conceptual Framework for Environmental Risk and Social Vulnerability Assessment in Complex Urban Settings. Urban Clim. 2018, 26, 161–173, doi:10.1016/j.uclim.2018.08.005.
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Ghandehari, M. Optical Phenomenology and Applications, Health Monitoring for Infrastructure Materials and the Environment. Smart Sens., Meas. Instrum. 2018, doi:10.1007/978-3-319-70715-0.
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Yang, L.; Li, W.; Ghandehari, M.; Fortino, G. People-Centric Cognitive Internet of Things for the Quantitative Analysis of Environmental Exposure. IEEE Internet Things J. 2018, 5, 2353–2366, doi:10.1109/jiot.2017.2751307.
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Ghandehari, M.; Aghamohamadnia, M.; Emig, T. Urban Radiation Sensing And Modeling. IGARSS 2018 - 2018 IEEE Int. Geosci. Remote Sens. Symp. 2018, 00, 1684–1687, doi:10.1109/igarss.2018.8517292.
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Liu, E.; Ghandehari, M.; Brückner, C.; Khalil, G.; Worlinsky, J.; Jin, W.; Sidelev, A.; Hyland, M.A. Mapping High PH Levels in Hydrated Calcium Silicates. Cem. Concr. Res. 2017, 95, 232–239, doi:10.1016/j.cemconres.2017.02.001.
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Ghandehari, M.; Aghamohamadnia, M.; Dobler, G.; Karpf, A.; Buckland, K.; Qian, J.; Koonin, S. Mapping Refrigerant Gases in the New York City Skyline. Sci. Rep. 2017, 7, 2735, doi:10.1038/s41598-017-02390-z.
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Johnson, N.E.; Ianiuk, O.; Cazap, D.; Liu, L.; Starobin, D.; Dobler, G.; Ghandehari, M. Patterns of Waste Generation: A Gradient Boosting Model for Short-Term Waste Prediction in New York City. Waste Manag.2017, 62, 3–11, doi:10.1016/j.wasman.2017.01.037.
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Sidelev, A.; Ghandehari, M. Quantitative Assessment of Subsurface Oxidation in Coated Materials. J. Perform. Constr. Facil. 2017, 31, 04017051, doi:10.1061/(asce)cf.1943-5509.0001024.
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Yang, L.; Li, W.; Duan, Y.; Luo, Y.; Ghandehari, M. Services D2D Aggregation for Environment Measurement Based on People-Centric IoT. 2017 IEEE 21st Int. Conf. Comput. Support. Cooperative Work Des. (CSCWD) 2017, 319–324, doi:10.1109/cscwd.2017.8066714.
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Ghandehari, M.; Emig, T.; Aghamohamadnia, M. Surface Temperatures in New York City: Geospatial Data Enables the Accurate Prediction of Radiative Heat Transfer. Sci. Rep. 2017, 8, 2224, doi:10.1038/s41598-018-19846-5.
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Dobler, G.; Ghandehari, M.; Koonin, S.; Sharma, M. A Hyperspectral Survey of New York City Lighting Technology. Sensors 2016, 16, 2047, doi:10.3390/s16122047.
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Rosso, F.; Pisello, A.; Jin, W.; Ghandehari, M.; Cotana, F.; Ferrero, M. Cool Marble Building Envelopes: The Effect of Aging on Energy Performance and Aesthetics. Sustainability 2016, 8, 753, doi:10.3390/su8080753.
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Narain, J.; Jin, W.; Ghandehari, M.; Wilke, E.; Shukla, N.; Berardi, U.; El-Korchi, T.; Dessel, S.V. Design and Application of Concrete Tiles Enhanced with Microencapsulated Phase-Change Material. J. Arch. Eng.2016, 22, 05015003, doi:10.1061/(asce)ae.1943-5568.0000194.
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Xie, K.; Li, C.; Ozbay, K.; Dobler, G.; Yang, H.; Chiang, A.-T.; Ghandehari, M. Development of a Comprehensive Framework for Video-Based Safety Assessment. 2016 IEEE 19th Int. Conf. Intell. Transp. Syst. (ITSC) 2016, 2638–2643, doi:10.1109/itsc.2016.7795980.
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Zulli, M.; Ghandehari, M.; Sidelev, A.; Shah, S.P. Dimensional Factors in Oxidation Induced Fracture in Reinforced Concrete. Constr. Build. Mater. 2016, 122, 264–272, doi:10.1016/j.conbuildmat.2016.05.077.
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Ghandehari, M.; Aghamohamadnia, M.; Dobler, G.; Karpf, A.; Cavalcante, C.; Buckland, K.; Qian, J.; Koonin, S. Ground Based Hyperspectral Imaging of Urban Emissions. 2016 8th Work. Hyperspectral Image Signal Process.: Evol. Remote Sens. (Whisp.) 2016, 1–3, doi:10.1109/whispers.2016.8071735.
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Aldea, C.; Ghandehari, M.; Shah, S.P.; Karr, A. Materials for Buildings and Structures. 2016, 170–174, doi:10.1002/3527606211.ch24.
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Zheng, S.; Ghandehari, M.; Ou, J. Photonic Crystal Fiber Long-Period Grating Absorption Gas Sensor Based on a Tunable Erbium-Doped Fiber Ring Laser. Sens. Actuators B: Chem. 2016, 223, 324–332, doi:10.1016/j.snb.2015.09.083.
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Rosso, F.; Jin, W.; Pisello, A.L.; Ferrero, M.; Ghandehari, M. Translucent Marbles for Building Envelope Applications: Weathering Effects on Surface Lightness and Finishing When Exposed to Simulated Acid Rain. Constr. Build. Mater. 2016, 108, 146–153, doi:10.1016/j.conbuildmat.2016.01.041.
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Dobler, G.; Ghandehari, M.; Koonin, S.E.; Nazari, R.; Patrinos, A.; Sharma, M.S.; Tafvizi, A.; Vo, H.T.; Wurtele, J.S. Dynamics of the Urban Lightscape. Inf. Syst. 2015, 54, 115–126, doi:10.1016/j.is.2015.06.002.
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Worlinsky, J.L.; Halepas, S.; Ghandehari, M.; Khalil, G.; Brückner, C. High PH Sensing with Water-Soluble Porpholactone Derivatives and Their Incorporation into a Nafion® Optode Membrane. Analyst 2015, 140, 190–196, doi:10.1039/c4an01462f.
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Leung, C.K.Y.; Wan, K.T.; Inaudi, D.; Bao, X.; Habel, W.; Zhou, Z.; Ou, J.; Ghandehari, M.; Wu, H.C.; Imai, M. Review: Optical Fiber Sensors for Civil Engineering Applications. Mater. Struct. 2015, 48, 871–906, doi:10.1617/s11527-013-0201-7.
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Zheng, S.; Shan, B.; Ghandehari, M.; Ou, J. Sensitivity Characterization of Cladding Modes in Long-Period Gratings Photonic Crystal Fiber for Structural Health Monitoring. Measurement 2015, 72, 43–51, doi:10.1016/j.measurement.2015.04.014.
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Klavarioti, M.; Kostarelos, K.; Pourjabbar, A.; Ghandehari, M. In Situ Sensing of Subsurface Contamination—Part I: Near-Infrared Spectral Characterization of Alkanes, Aromatics, and Chlorinated Hydrocarbons. Environ. Sci. Pollut. Res. 2014, 21, 5849–5860, doi:10.1007/s11356-013-2478-z.
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Experience
New York University, Tandon School of Engineering
Department of Civil and Urban Engineering
Associate Professor
September 1999 to present
Center for Urban Science and Progress
Inaugural Faculty& Head of Urban Observatory
January 2013 to 2019
Northwestern University
Post Doctoral Fellow
December 1998 to July 1999
Weidlinger Associates, New York
Engineer
April 1989 to June 1991
Geiger and Associates, New York
Engineer
June 1988 to April 1989
Patents
Method for Mapping Distribution of High pH, (Pending)
Technology for detection of hazardous substances, (Provisional)
Research News
Subway air pollution disproportionately impacts New York City's minority and low-income commuters
A comprehensive study on New York City's subway air quality has revealed that longer commute times lead to higher exposure to hazardous air pollutants, a problem that disproportionately affects minority and low-income communities who endure more prolonged and frequent travel through the system.
In a paper published in PLOS ONE, NYU Tandon School of Engineering researchers modeled subway riders' typical daily commutes to determine exposure to particulate matter pollution (PM2.5). This was done by integrating home-to-work commute data with pollutant measurements the researchers collected from all platforms and within a typical car in all train lines throughout the NYC subway system.
Masoud Ghandehari -- a professor in NYU Tandon’s Civil and Urban Engineering Department and a member of C2SMARTER, a Tier 1 University Transportation Center designated by the U.S. Department of Transportation -- led the research team. Other researchers on the paper include its first author, Shams Azad, who earned a Ph.D. in Transportation Engineering from NYU Tandon in 2023, and Pau Ferrar-Cid, Machine Learning Researcher at Universitat Politècnica de Catalunya in Spain who was an NYU Tandon visiting scholar in 2022.
PM2.5 refers to tiny particles suspended in the air that, when inhaled, can enter the lungs and potentially the bloodstream, causing a range of short- and long-term health complications. These include respiratory and cardiovascular diseases, and some components have also been identified as neurotoxins.
PM2.5 are traditionally byproducts of fossil fuel combustion. In the subway system, however, the particles are introduced as a result of abrasion of breaks, rails, and wheels, contributing to very high iron content in the collected and analyzed particles.
An NYC Air Pollution Exposure Map the researchers created can be used to calculate personal exposure for any origin and destination within New York City.
The burden of disease due to exposure to poor air quality in the subway system does not fall equally, the study found. Black and Hispanic workers face PM2.5 exposure levels 35% and 23% higher, respectively, compared to their Asian and white counterparts, according to the study.
This disparity stems from differences in commuting trends, duration of subway travel, and the varying pollution levels across stations and lines. Minority workers residing in low-income communities often endure longer commutes, transferring through stations (which are more polluted than subway cars) in order to reach job hubs like downtown Manhattan.
Economically disadvantaged communities generally are exposed to more pollutants compared to affluent workers. A positive correlation was identified between the percentage of residents below the poverty line and higher levels of PM2.5 exposure.
This discrepancy is partly attributed to the reliance on the subway system among lower-income populations, who have limited access to alternative transportation options like private vehicles or carpool services. Conversely, many affluent workers can avoid lengthy subway commutes by living in proximity to their workplaces.
In fact, residents in upper Manhattan neighborhoods, including Washington Heights and Inwood — two communities with poverty rates above citywide averages — have the highest per capita levels of subway pollutant commuting exposure, the study shows. This is due to a combination of a large number of commuters and longer commute time. Midtown Manhattan — where many people live close to workplaces — and portions of Queens without easily accessible subway stations have some of the lowest per capita exposure levels.
Measurements were carried out in December 2021 and June 2022, sampling 19 distinct subway lines and 368 stations. The researchers took end-to-end round trips on each of the lines studied, measuring the PM2.5 concentration at one-second sampling intervals. In one direction they stayed on the train from start to end. On the return trip they got off at each station and waited until the arrival of the next train, measuring the platform concentrations at the same sampling interval.
Sampling of the platform air at one-second intervals confirmed that pollutant concentration peaks when the train arrives, where the train churns up the pollutants deposited in the tunnel throughout years of service. The concentration values from the 2021 study can be found in a paper published in Atmospheric Pollution Research.
To calculate PM2.5 exposure, researchers used origin-destination records from the U.S. Census Bureau (specifically the 2019 LEHD Origin-Destination (OD) dataset) to simulate home-to-job commutes of over 3 million workers in Manhattan, Brooklyn, the Bronx, and Queens in 2019, calculating the average per capita daily PM2.5 exposure in 34,000 census blocks.
This study was a collaboration between the NYU Tandon School of Engineering and the NYU Grossman School of Medicine, in partnership with the Polytechnic University of Catalunya. The work was funded by NYU Tandon's C2SMART Center, with a grant from the U.S. Department of Transportation’s University Transportation Centers Program under Grant Number 69A3551747124, and by the National Science Foundation (award number 1856032). For more information, see also A Comprehensive Analysis of the Air Quality in the NYC Subway System (September 2022).
Azad, S., Ferrer-Cid, P., & Ghandehari, M. (2024). Exposure to fine particulate matter in the New York City subway system during home-work commute. PLOS ONE, 19(8), e0307096. https://doi.org/10.1371/journal.pone.0307096
A study on the association of socioeconomic and physical cofactors contributing to power restoration after Hurricane Maria
This research was led by Masoud Ghandehari, professor in the Department of Civil and Urban Engineering at NYU Tandon, with Shams Azad, a Ph.D. student under Ghandehari’s guidance.
The electric power infrastructure in Puerto Rico suffered substantial damage as Hurricane Maria crossed the island on September 20, 2017. Despite significant efforts made by authorities, it took almost a year to achieve near-complete power recovery. The electrical power failure contributed to the loss of life and the slow pace of disaster recovery. Hurricane Maria caused extensive damage to Puerto Rico’s power lines, leaving on average 80% of the distribution system out of order for months.
In this study, imagery of daily nighttime lights from space was used to measure the loss and restoration of electric power every day at 500-meter spatial resolution. The researchers monitored the island’s 889 county subdivisions for over eight months using Visible/Infrared Imagery and Radiometer Suite sensors — which showed how power was absent/present visually — and by formulating a regression model to identify the status of the power recovery effort.
The hurricane hit the island with its maximum strength at the point of landfall, which corresponds to massive destruction across all physical infrastructures, resulting in a longer recovery period. Indeed, the researchers found that every 50-kilometer increase in distance from the landfall corresponded to 30% fewer days without power. Road connectivity was a major issue for the restoration effort: areas having a direct connection with hi-speed roads recovered more quickly with 7% fewer outage days. Areas that were affected by moderate landslides needed 5.5%, and high landslide areas needed 11.4% more days to recover.
The researchers found that financially disadvantaged areas suffered more from the extended outage. For every 10% increase in population below the poverty line, there was a 2% increase in recovery time. While financial status did impact restoration efforts, the investigators did not find any additional association of race or ethnicity in the study.
Research Centers, Labs, and Groups
Center for Urban Science + Progress (CUSP)
The Center for Urban Science and Progress (CUSP) is an interdisciplinary research ...
