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Spatial Distribution of the Annual Atmospheric Carbon Dioxide in the Contiguous USA and Their Controlling Factors

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Abstract

Analyzing the geographical distribution of carbon dioxide (CO2) is essential for understanding the influence and structural nature of area-specific sectoral emissions in the total carbon budget. In this practice, the implementations of specific accounting and spatial attribution methods are criticized for multiple sources of uncertainties. Therefore, estimated emissions may bring forth unreliable conclusions to analyze and understand the spatial variation and associated hypothesized anthropogenic sources in this gas’ biophysical cyclical process. This paper examined the relations among the observed atmospheric phenomenon of column-averaged CO2 (XCO2) and the hypothesized major anthropogenic emissions and sources to understand their influences on the spatial variation of annual XCO2 concentration at the county-level contiguous USA (CONUS). Specifically, this paper explored the spatial distribution of XCO2 in the CONUS by processing the Orbiting Carbon Observatory-2 (OCO-2) satellite-based observed database of XCO2. The study found that observed XCO2 and interpolated data can represent the annual spatial variation of XCO2 at the county level of CONUS. The study established that industrial locations and emissions play a major role in the spatial variation of XCO2 in the CONUS regardless of the direct emissions from other anthropogenic sources (i.e., transport, residential) and the urban and rural nature of the counties. The study found that in 2017, 2634.92 Million Tons (MTons) industrial emissions increased the level of XCO2 by 0.0621 ppm in the CONUS.

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Data Availability

The raw databases are available on different organizational websites. The available datasets’ web links are provided in the manuscript. The datasets generated and analyzed for this study are available from the corresponding author on reasonable request.

References

  1. Boychuk, P., Boychuk, K., Nahorski, Z., & Horabik, J. (2012). Spatial inventory of greenhouse gas emissions from the road transport in Poland. ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes, 1(4), 9–15.

    Google Scholar 

  2. Brophy, K., Graven, H., Manning, A. J., White, E., Arnold, T., Fischer, M. L., Jeong, S., Cui, X., & Rigby, M. (2019). Characterizing uncertainties in atmospheric inversions of fossil fuel CO2 emissions in California. Atmospheric Chemistry and Physics, 19(5), 2991–3006. https://doi.org/10.5194/acp-19-2991-2019

    Article  CAS  Google Scholar 

  3. Cervero, R., & Murakami, J. (2010). Effects of built environments on vehicle miles traveled: Evidence from 370 US urbanized areas. Environment and Planning A, 42(2), 400–418.

    Article  Google Scholar 

  4. CO2 Virtual Science Data Environment. (n.d.). Retrieved August 21, 2019, from https://co2.jpl.nasa.gov/#mission=OCO-2

  5. Council, N. R. (2010). Verifying greenhouse gas emissions: Methods to support international climate agreements. https://doi.org/10.17226/12883

  6. Danylo, O., Bun, R., See, L., & Charkovska, N. (2019). High-resolution spatial distribution of greenhouse gas emissions in the residential sector. Mitigation and Adaptation Strategies for Global Change, 24(6), 941–967. https://doi.org/10.1007/s11027-019-9846-z

    Article  Google Scholar 

  7. Dubey, M. K., Lindenmaier, R., Henderson, B. G., Green, D., Allen, N. T., Roehl, C. M., Blavier, J.-F., Butterfield, Z. T., Love, S., Hamelmann, J. D., & Wunch, D. (2017). TCCON data from Four Corners (US), Release GGG2014.R0. CaltechDATA. https://doi.org/10.14291/tccon.ggg2014.fourcorners01.r0/1149272

  8. FHWA. (n.d.). HPMS public release of geospatial data in shapefile format—Policy | Federal Highway Administration. Retrieved August 21, 2019, from https://www.fhwa.dot.gov/policyinformation/hpms/shapefiles.cfm

  9. Ghosh, T., Elvidge, C. D., Sutton, P. C., Baugh, K. E., Ziskin, D., & Tuttle, B. T. (2010). Creating a global grid of distributed fossil fuel CO2 emissions from nighttime satellite imagery. Energies, 3(12), 1895–1913. https://doi.org/10.3390/en3121895

    Article  CAS  Google Scholar 

  10. Gregg, J. S., Losey, L. M., Andres, R. J., Blasing, T. J., & Marland, G. (2009). The temporal and spatial distribution of carbon dioxide emissions from fossil-fuel use in North America. Journal of Applied Meteorology and Climatology, 48(12), 2528–2542. https://doi.org/10.1175/2009JAMC2115.1

    Article  Google Scholar 

  11. Gurney, K. R., Razlivanov, I., Song, Y., Zhou, Y., Benes, B., & Abdul-Massih, M. (2012). Quantification of fossil fuel CO2 emissions on the building/street scale for a large U.S. city. Environmental Science & Technology, 46(21), 12194–12202. https://doi.org/10.1021/es3011282

  12. Hansen, J., Sato, M., Kharecha, P., Beerling, D., Berner, R., Masson-Delmotte, V., Pagani, M., Raymo, M., Royer, D. L., & Zachos, J. C. (2008). Target atmospheric CO2: Where should humanity aim? The Open Atmospheric Science Journal, 2(1), 217–231. https://doi.org/10.2174/1874282300802010217

    Article  CAS  Google Scholar 

  13. Hockstad, L., & Hanel, L. (2018). Inventory of US greenhouse gas emissions and sinks. Environmental System Science Data Infrastructure for a Virtual Ecosystem.

  14. Hogue, S., Marland, E., Andres, R. J., Marland, G., & Woodard, D. (2016). Uncertainty in gridded CO2 emissions estimates. Earth’s Future, 4(5), 225–239. https://doi.org/10.1002/2015EF000343

    Article  CAS  Google Scholar 

  15. Hutchins, M. G., Colby, J. D., Marland, G., & Marland, E. (2017). A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States. Mitigation and Adaptation Strategies for Global Change, 22(6), 947–972. https://doi.org/10.1007/s11027-016-9709-9

    Article  Google Scholar 

  16. Iraci, L. T., Podolske, J. R., Hillyard, P. W., Roehl, C., Wennberg, P. O., Blavier, J.-F., Landeros, J., Allen, N., Wunch, D., Zavaleta, J., Quigley, E., Osterman, G. B., Albertson, R., Dunwoody, K., & Boyden, H. (2017). TCCON data from Edwards (US), Release GGG2014.R1. CaltechDATA. https://doi.org/10.14291/tccon.ggg2014.edwards01.r1/1255068

  17. Iraci, L. T., Podolske, J. R., Hillyard, P. W., Roehl, C., Wennberg, P. O., Blavier, J.-F., Landeros, J., Allen, N., Wunch, D., Zavaleta, J., Quigley, E., Osterman, G. B., Barrow, E., & Barney, J. (2017). TCCON data from Indianapolis (US), Release GGG2014.R1. CaltechDATA. https://doi.org/10.14291/tccon.ggg2014.indianapolis01.r1/1330094

  18. Jacox, E. H., & Samet, H. (2007). Spatial join techniques. ACM Transactions on Database Systems, 32(1), 7. https://doi.org/10.1145/1206049.1206056

    Article  Google Scholar 

  19. LeSage, J., & Pace, R. K. (2010). Introduction to spatial econometrics, by James LeSage and R. Kelly Pace. Journal of Regional Science, 50(5), 1014–1015. https://doi.org/10.1111/j.1467-9787.2010.00709_7.x

    Article  Google Scholar 

  20. Maes, J., Vliegen, J., van de Vel, K., Janssen, S., Deutsch, F., de Ridder, K., & Mensink, C. (2009). Spatial surrogates for the disaggregation of CORINAIR emission inventories. Atmospheric Environment, 43(6), 1246–1254.

    Article  CAS  Google Scholar 

  21. Marland, G. (2008). Uncertainties in accounting for CO2 from fossil fuels. Journal of Industrial Ecology, 12(2), 136–139. https://doi.org/10.1111/j.1530-9290.2008.00014.x

    Article  CAS  Google Scholar 

  22. Osterman, G., O'Dell, C., Eldering, A., Fisher, B., Crisp, D., Cheng, C., Frankenberg, C., Lambert, A., Gunson, M., & Mandrake, L. (2020). Orbiting Carbon Observatory-2& 3 (OCO-2 & OCO-3)–Data Product User's Guide, Operational Level 2 Data Versions 10 and Lite File Version 10 and VEarly. Version 1.0, Revision A, 8 June 2020. Pasadena, CA: Jet Propulsion Laboratory.

  23. Parshall, L., Gurney, K., Hammer, S. A., Mendoza, D., Zhou, Y., & Geethakumar, S. (2010). Modeling energy consumption and CO2 emissions at the urban scale: Methodological challenges and insights from the United States. Energy Policy, 38(9), 4765–4782. https://doi.org/10.1016/j.enpol.2009.07.006

    Article  Google Scholar 

  24. Rayner, P. J., & O’Brien, D. M. (2001). The utility of remotely sensed CO2 concentration data in surface source inversions. Geophysical Research Letters, 28(1), 175–178. https://doi.org/10.1029/2000GL011912

    Article  CAS  Google Scholar 

  25. Reis, S., Pfeiffer, H., Theloke, J., & Scholz, Y. (2008). Temporal and spatial distribution of carbon emissions. In A. J. Dolman, R. Valentini, & A. Freibauer (Eds.), The Continental-Scale Greenhouse Gas Balance of Europe (pp. 73–90). Springer New York. https://doi.org/10.1007/978-0-387-76570-9_5

  26. Running, S., & Q. M. (2015). MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD17A2H.006

  27. US EPA, O. (2015, May 18). GHG Reporting Program Data Sets [Overviews and Factsheets]. US EPA. https://www.epa.gov/ghgreporting/ghg-reporting-program-data-sets

  28. US EPA, O. (2017, February 8). Inventory of U.S. Greenhouse Gas Emissions and Sinks [Reports and Assessments]. US EPA. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks

  29. Wennberg, P. O., Roehl, C. M., Wunch, D., Toon, G. C., Blavier, J.-F., Washenfelder, R., Keppel-Aleks, G., Allen, N. T., & Ayers, J. (2017). TCCON data from Park Falls (US), Release GGG2014.R1. CaltechDATA. https://doi.org/10.14291/tccon.ggg2014.parkfalls01.r1

  30. Wennberg, P. O., Wunch, D., Roehl, C. M., Blavier, J.-F., Toon, G. C., & Allen, N. T. (2017a). TCCON data from Caltech (US), Release GGG2014.R1. CaltechDATA. https://doi.org/10.14291/tccon.ggg2014.pasadena01.r1/1182415

  31. Wennberg, P. O., Wunch, D., Roehl, C. M., Blavier, J.-F., Toon, G. C., & Allen, N. T. (2017b). TCCON data from Lamont (US), Release GGG2014.R1. CaltechDATA. https://doi.org/10.14291/tccon.ggg2014.lamont01.r1/1255070

  32. Where greenhouse gases come from—US. Energy Information Administration (EIA). (n.d.). Retrieved November 3, 2019, from https://www.eia.gov/energyexplained/energy-and-the-environment/where-greenhouse-gases-come-from.php

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Acknowledgements

The study was conducted on several secondary databases. Therefore, we are grateful to the organizations and anonymous persons who have made the databases open and available to the people. Specifically, we would like to extend our heartiest gratitude to The National Aeronautics and Space Administration (NASA), United States Environmental Protection Agency (USEPA), Federal Highway Administration (FHWA), and US Bureau of Census for their accessible online platforms of databases. Finally, we would like to thank the anonymous reviewers, editor-in-chief, and advisory editors for their scholarly suggestions and editorial corrections.

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Authors and Affiliations

  1. Department of Geography and Planning, University of Toledo, Toledo, OH, 43606, USA

    Muhammad Salaha Uddin & Oleg Smirnov

  2. Department of Urban & Regional Planning, Khulna University of Engineering & Technology, Khulna, Bangladesh

    Muhammad Salaha Uddin

  3. Department of Economics, University of Toledo, Toledo, OH, 43606, USA

    Oleg Smirnov

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  1. Muhammad Salaha Uddin
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Contributions

Muhammad Salaha Uddin: conceptualization, methodology, software, formal analysis, data curation, writing the original draft, writing review and editing, visualization. Oleg Smirnov: methodology, formal analysis, supervision.

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Correspondence to Muhammad Salaha Uddin.

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Uddin, M.S., Smirnov, O. Spatial Distribution of the Annual Atmospheric Carbon Dioxide in the Contiguous USA and Their Controlling Factors. Environ Model Assess 27, 57–76 (2022). https://doi.org/10.1007/s10666-021-09780-8

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