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Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer

Despite decades of research seeking to derive the urban energy budget, the dynamics of thermal exchange in the densely constructed environment is not yet well understood. Using New York City as a study site, we present a novel hybrid experimental-computational approach for a better understanding of...

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Autores principales: Ghandehari, Masoud, Emig, Thorsten, Aghamohamadnia, Milad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5797139/
https://www.ncbi.nlm.nih.gov/pubmed/29396467
http://dx.doi.org/10.1038/s41598-018-19846-5
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author Ghandehari, Masoud
Emig, Thorsten
Aghamohamadnia, Milad
author_facet Ghandehari, Masoud
Emig, Thorsten
Aghamohamadnia, Milad
author_sort Ghandehari, Masoud
collection PubMed
description Despite decades of research seeking to derive the urban energy budget, the dynamics of thermal exchange in the densely constructed environment is not yet well understood. Using New York City as a study site, we present a novel hybrid experimental-computational approach for a better understanding of the radiative heat transfer in complex urban environments. The aim of this work is to contribute to the calculation of the urban energy budget, particularly the stored energy. We will focus our attention on surface thermal radiation. Improved understanding of urban thermodynamics incorporating the interaction of various bodies, particularly in high rise cities, will have implications on energy conservation at the building scale, and for human health and comfort at the urban scale. The platform presented is based on longwave hyperspectral imaging of nearly 100 blocks of Manhattan, in addition to a geospatial radiosity model that describes the collective radiative heat exchange between multiple buildings. Despite assumptions in surface emissivity and thermal conductivity of buildings walls, the close comparison of temperatures derived from measurements and computations is promising. Results imply that the presented geospatial thermodynamic model of urban structures can enable accurate and high resolution analysis of instantaneous urban surface temperatures.
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spelling pubmed-57971392018-02-12 Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer Ghandehari, Masoud Emig, Thorsten Aghamohamadnia, Milad Sci Rep Article Despite decades of research seeking to derive the urban energy budget, the dynamics of thermal exchange in the densely constructed environment is not yet well understood. Using New York City as a study site, we present a novel hybrid experimental-computational approach for a better understanding of the radiative heat transfer in complex urban environments. The aim of this work is to contribute to the calculation of the urban energy budget, particularly the stored energy. We will focus our attention on surface thermal radiation. Improved understanding of urban thermodynamics incorporating the interaction of various bodies, particularly in high rise cities, will have implications on energy conservation at the building scale, and for human health and comfort at the urban scale. The platform presented is based on longwave hyperspectral imaging of nearly 100 blocks of Manhattan, in addition to a geospatial radiosity model that describes the collective radiative heat exchange between multiple buildings. Despite assumptions in surface emissivity and thermal conductivity of buildings walls, the close comparison of temperatures derived from measurements and computations is promising. Results imply that the presented geospatial thermodynamic model of urban structures can enable accurate and high resolution analysis of instantaneous urban surface temperatures. Nature Publishing Group UK 2018-02-02 /pmc/articles/PMC5797139/ /pubmed/29396467 http://dx.doi.org/10.1038/s41598-018-19846-5 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ghandehari, Masoud
Emig, Thorsten
Aghamohamadnia, Milad
Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer
title Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer
title_full Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer
title_fullStr Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer
title_full_unstemmed Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer
title_short Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer
title_sort surface temperatures in new york city: geospatial data enables the accurate prediction of radiative heat transfer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5797139/
https://www.ncbi.nlm.nih.gov/pubmed/29396467
http://dx.doi.org/10.1038/s41598-018-19846-5
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