Show simple item record

Siting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area

dc.contributor.authorMueller, K.
dc.contributor.authorYadav, V.
dc.contributor.authorLopez‐coto, I.
dc.contributor.authorKarion, A.
dc.contributor.authorGourdji, S.
dc.contributor.authorMartin, C.
dc.contributor.authorWhetstone, J.
dc.date.accessioned2018-04-04T18:48:23Z
dc.date.available2019-05-13T14:45:25Zen
dc.date.issued2018-03-16
dc.identifier.citationMueller, K.; Yadav, V.; Lopez‐coto, I. ; Karion, A.; Gourdji, S.; Martin, C.; Whetstone, J. (2018). "Siting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area." Journal of Geophysical Research: Atmospheres 123(5): 2910-2926.
dc.identifier.issn2169-897X
dc.identifier.issn2169-8996
dc.identifier.urihttps://hdl.handle.net/2027.42/142902
dc.description.abstractThere is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridorâ Baltimore/Washington, DC (NECâ B/W), downwind of large fluxes. To help site background towers for the NECâ B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, rootâ meanâ square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.Key PointsFactoring in the variability of greenhouse gas enhancements in incoming air is critical for estimating emissions in an urban domainStatistical methods were used to site four towers sampling background air in the Washington, DC/Baltimore regionOptimal background tower configurations for representing incoming air can still have large errors for any given urban GHG observation
dc.publisherWiley Periodicals, Inc.
dc.subject.otherurban
dc.subject.otherinversion
dc.subject.otheremissions
dc.subject.othercarbon dioxide
dc.subject.otherbackground
dc.subject.othergreenhouse gas
dc.titleSiting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelAtmospheric and Oceanic Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/1/jgrd54353_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/2/jgrd54353.pdf
dc.identifier.doi10.1002/2017JD027364
dc.identifier.sourceJournal of Geophysical Research: Atmospheres
dc.identifier.citedreferenceMcKain, K., Down, A., Raciti, S. M., Budney, J., Hutyra, L. R., Floerchinger, C., â ¦ Wofsy, S. C. ( 2015 ). Methane emissions from natural gas infrastructure and use in the urban region of Boston, Massachusetts. PNAS, 112 ( 7 ), 1941 â 1946. https://doi.org/10.1073/pnas.1416261112
dc.identifier.citedreferenceBreón, F. M., Broquet, G., Puygrenier, V., Chevallier, F., Xuerefâ Remy, I., Ramonet, M., â ¦ Ciais, P. ( 2015 ). An attempt at estimating Paris area CO 2 emissions from atmospheric concentration measurements. Atmospheric Chemistry and Physics, 15 ( 4 ), 1707 â 1724. https://doi.org/10.5194/acpâ 15â 1707â 2015
dc.identifier.citedreferenceCambaliza, M. O., Shepson, P. B., Caulton, D., Stirm, B., Samarov, D., Gurney, K., â ¦ Richardson, S. J. ( 2014 ). Assessment of uncertainties of an aircraftâ based massâ balance approach for quantifying urban greenhouse gas emissions. Atmospheric Chemistry and Physics, 14, 9029 â 9050, www.atmosâ chemâ phys.net/14/9029/2014/. https://doi.org/10.5194/acpâ 14â 9029â 2014
dc.identifier.citedreferenceDavis, K. J., Deng, A., Lauvaux, T., Miles, N. L., Richardson, S. J., Sarmiento, D. P., â ¦ Karion, A. ( 2017 ). The Indianapolis Flux Experiment (INFLUX): A testâ bed for developing urban greenhouse gas emission measurements. Elementa: Science of the Anthropocene, 5, 21. https://doi.org/10.1525/elementa.188
dc.identifier.citedreferenceDuren, R. M., & Miller, C. E. ( 2012 ). Measuring the carbon emissions of megacities. Nature Climate Change, 2 ( 8 ), 560 â 562. https://doi.org/10.1038/nclimate1629
dc.identifier.citedreferenceFang, Y., Michalak, A. M., Shiga, Y. P., & Yadav, V. ( 2014 ). Using atmospheric observations to evaluate the spatiotemporal variability of CO 2 fluxes simulated by terrestrial biospheric models. Biogeosciences, 11 ( 23 ), 6985 â 6997. https://doi.org/10.5194/bgâ 11â 6985â 2014
dc.identifier.citedreferenceGerbig, C., Dolman, A. J., & Heimann, M. ( 2009 ). On observational and modelling strategies targeted at regional carbon exchange over continents. Biogeosciences, 6 ( 10 ), 1949 â 1959. https://doi.org/10.5194/bgâ 6â 1949â 2009
dc.identifier.citedreferenceGerbig, C., Lin, J. C., Wofsy, S. C., Daube, B. C., Andrews, A. E., Stephens, B. B., â ¦ Grainger, C. A. ( 2003 ). Toward constraining regionalâ scale fluxes of CO 2 with atmospheric observations over a continent: 2. Analysis of COBRA data using a receptorâ oriented framework. Journal of Geophysical Research, 108 ( D24 ), 4757. https://doi.org/10.1029/2003JD003770
dc.identifier.citedreferenceGourdji, S. M., Mueller, K. L., Yadav, V., Huntzinger, D. N., Andrews, A. E., Trudeau, M., â ¦ Michalak, A. M. ( 2012 ). North American CO 2 exchange: Interâ comparison of modeled estimates with results from a fineâ scale atmospheric inversion. Biogeosciences, 9 ( 1 ), 457 â 475. https://doi.org/10.5194/bgâ 9â 457â 2012
dc.identifier.citedreferenceGurney, K. R., Mendoza, D. L., Zhou, Y., Fischer, M. L., Miller, C. C., Geethakumar, S., & de la Rue du Can, S. ( 2009 ). High resolution fossil fuel combustion CO 2 emission fluxes for the United States. Environmental Science & Technology, 43 ( 14 ), 5535 â 5541. https://doi.org/10.1021/es900806c
dc.identifier.citedreferenceHuntzinger, D. N., Gourdji, S. M., Mueller, K. L., & Michalak, A. M. ( 2011 ). The utility of continuous atmospheric measurements for identifying biospheric CO 2 flux variability. Journal of Geophysical Research, 116, D06110. https://doi.org/10.1029/2010JD015048
dc.identifier.citedreferenceLauvaux, T., Miles, N. L., Deng, A., Richardson, S. J., Cambaliza, M. O., Davis, K. J., â ¦ Wu, K. ( 2016 ). High resolution atmospheric inversion of urban CO 2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX). Journal of Geophysical Research, 121, 5213 â 5236. https://doi.org/10.1002/2015JD024473
dc.identifier.citedreferenceLin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis, K. J., & Grainger, C. A. ( 2003 ). A nearâ field tool for simulating the upstream influence of atmospheric observations: The Stochastic Timeâ Inverted Lagrangian Transport (STILT) model. Journal of Geophysical Research, 108 ( D16 ), 4493. https://doi.org/10.1029/2002JD003161
dc.identifier.citedreferenceLopezâ Coto, I., Ghosh, S., Prasad, K., & Whetstone, J. ( 2017 ). Towerâ based greenhouse gas measurement network designâ The NIST North East Corridor testbed. Advances in Atmospheric Sciences, 34 ( 9 ), 1095 â 1105. https://doi.org/10.1007/s00376â 017â 6094â 6
dc.identifier.citedreferenceMueller, K. L., Yadav, V., Curtis, P. S., Vogel, C., & Michalak, A. M. ( 2010 ). Attributing the variability of eddyâ covariance CO 2 flux measurements across temporal scales using geostatistical regression for a mixed northern hardwood forest. Global Biogeochemical Cycles, 24, GB3023. https://doi.org/10.1029/2009GB003642
dc.identifier.citedreferencePeters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J., Masarie, K., â ¦ Tans, P. P. ( 2007 ). An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker. Proceedings of the National Academy of Sciences of the United States of America, 104 ( 48 ), 18,925 â 18,930. https://doi.org/10.1073/pnas.0708986104
dc.identifier.citedreferenceStein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D., & Ngan, F. ( 2015 ). NOAA’s HYSPLIT atmospheric transport and dispersion modelling system. Bulletin of the American Meteorological Society, 96 ( 12 ), 2059 â 2077. https://doi.org/10.1175/BAMSâ Dâ 14â 00110.1
dc.identifier.citedreferenceTurnbull, J. C., Sweeney, C., Karion, A., Newberger, T., Lehmann, S. J., Tans, P. P., â ¦ Razlivanov, I. ( 2015 ). Toward quantification and source sector identification of fossil fuel CO 2 emissions from an urban area: Results from the INFLUX experiment. Journal of Geophysical Research: Atmospheres, 120, 292 â 312. https://doi.org/10.1002/2014JD022555
dc.identifier.citedreferenceVerhulst, K., Karion, A., Kim, J., Salameh, P. K., Sloop, C., Pongetti, T., â ¦ Miller, C. ( 2016 ). Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project: 1. Calibration, urban enhancements, and uncertainty estimates. Atmospheric Chemistry and Physics Discussions, 1 â 61. https://doi.org/10.5194/acpâ 2016â 850
dc.identifier.citedreferenceYadav, V., Mueller, K. L., Dragoni, D., & Michalak, A. M. ( 2010 ). A geostatistical synthesis study of factors affecting gross primary productivity in various ecosystems of North America. Biogeosciences, 7 ( 9 ), 2655 â 2671. https://doi.org/10.5194/bgâ 7â 2655â 2010
dc.identifier.citedreferenceYadav, V., Mueller, K. L., & Michalak, A. M. ( 2013 ). A backward elimination discrete optimization algorithm for model selection in spatioâ temporal regression models. Environmental Modelling & Software, 42, 88 â 98. https://doi.org/10.1016/j.envsoft.2012.12.009
dc.identifier.citedreferenceZeng, N., Mariotti, A., & Wetzel, P. ( 2005 ). Terrestrial mechanisms of interannual CO 2 variability. Global Biogeochemical Cycles, 19, GB1016. https://doi.org/10.1029/2004GB002273
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.