2026-09-19T13:29:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/906592022-01-13T07:53:59Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Marguerite Nyhan.
Keeler, Rachel H. (Rachel Heiden)
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences.
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
n-us-ny
2014-10-08T15:21:02Z
2014-10-08T15:21:02Z
2014
2014
http://hdl.handle.net/1721.1/90659
890397821
Thesis: S.B., Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences, 2014.
55
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 28-33).
A machine-learning model was created to predict air pollution at high spatial resolution in Manhattan, New York using taxi trip data. Urban air pollution increases morbidity and mortality through respiratory and cardiovascular impacts, and understanding and predicting it is a significant public health challenge. A neural network NARX model was created in MATLAB for each cell on a 250m square grid laid over Manhattan, for a total of 907 individual models across the city, for PM2 .5 , CO, NO2 , 03, and SO 2. In addition to standard meteorological inputs, data describing the distance and time traveled by taxis within each grid cell was used in the models. The models generally performed well, with mean R2 values between .62 (SO 2) and .86 (03), comparable to or better than previous models at this spatial scale. The model is computationally efficient enough to be run in real-time to aid citizens' and public health officials' decisions, and its efficacy suggests that taxi data is a valuable additional input to previous neural network pollution models.
by Rachel H. Keeler.
S.B.
33 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Earth, Atmospheric, and Planetary Sciences.
A machine learning model of Manhattan air pollution at high spatial resolution
Thesis
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<Abstract>A machine-learning model was created to predict air pollution at high spatial resolution in Manhattan, New York using taxi trip data. Urban air pollution increases morbidity and mortality through respiratory and cardiovascular impacts, and understanding and predicting it is a significant public health challenge. A neural network NARX model was created in MATLAB for each cell on a 250m square grid laid over Manhattan, for a total of 907 individual models across the city, for PM2 .5 , CO, NO2 , 03, and SO 2. In addition to standard meteorological inputs, data describing the distance and time traveled by taxis within each grid cell was used in the models. The models generally performed well, with mean R2 values between .62 (SO 2) and .86 (03), comparable to or better than previous models at this spatial scale. The model is computationally efficient enough to be run in real-time to aid citizens' and public health officials' decisions, and its efficacy suggests that taxi data is a valuable additional input to previous neural network pollution models.</Abstract>
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