Anthropogenic CO2 emissions from a megacity in the Yangtze River Delta of China
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Anthropogenic CO2 emissions from cities represent a major source contributing to the global atmospheric CO2 burden. Here, we examined the enhancement of atmospheric CO2 mixing ratios by anthropogenic emissions within the Yangtze River Delta (YRD), China, one of the world’s most densely populated regions (population greater than 150 million). Tower measurements of CO2 mixing ratios were conducted from March 2013 to August 2015 and were combined with numerical source footprint modeling to help constrain the anthropogenic CO2 emissions. We simulated the CO2 enhancements (i.e., fluctuations superimposed on background values) for winter season (December, January, and February). Overall, we observed mean diurnal variation of CO2 enhancement of 23.5~49.7 μmol mol−1, 21.4~52.4 μmol mol−1, 28.1~55.4 μmol mol−1, and 29.5~42.4 μmol mol−1 in spring, summer, autumn, and winter, respectively. These enhancements were much larger than previously reported values for other countries. The diurnal CO2 enhancements reported here showed strong similarity for all 3 years of the study. Results from source footprint modeling indicated that our tower observations adequately represent emissions from the broader YRD area. Here, the east of Anhui and the west of Jiangsu province contributed significantly more to the anthropogenic CO2 enhancement compared to the other sectors of YRD. The average anthropogenic CO2 emission in 2014 was 0.162 (± 0.005) mg m−2 s−1 and was 7 ± 3% higher than 2010 for the YRD. Overall, our emission estimates were significantly smaller (9.5%) than those estimated (0.179 mg m−2 s−1) from the EDGAR emission database.
KeywordsAnthropogenic CO2 emissions Megacity WRF-STILT model Tall tower observations Yangtze River Delta China
We would like to express our sincere thanks to Professor Timothy J. Griffis and Professor Xuhui Lee for advice in improving this paper’s logical organization and language, and also thank Professor R. Nassar for providing the hourly scaling factors for the different anthropogenic CO2 source categories. The tall tower data can be accessed at our group website (https://yncenter.sites.yale.edu/zh-hans/data-access).
This research was supported by National Natural Science Foundation of China (grants 41505005 and 41475141), the U.S. National Science Foundation (grants 1640337 and ATM-0546476), the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology (grant no. 2014r046), the Natural Science Foundation of Jiangsu Province, China (grant BK20150900), the Ministry of Education of China under grant PCSIRT, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
- Bagley JE, Jeong S, Cui X, Newman S, Zhang J, Priest C, Campos-Pineda M, Andrews AE, Bianco L, Lloyd M, Lareau N, Clements C, Fischer ML (2017) Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions. J Geophys Res Atmos 122(3):1901–1918CrossRefGoogle Scholar
- Brown K, et al. (2012) UK Greenhouse Gas Inventory, 1990 to 2010: annual report for submission under the framework convention on climate change. Didcot, AEA Technology, 357 pp.Google Scholar
- Chen B, Zhang H, Coops NC, Fu D, Worthy DEJ, Xu G et al (2014) Assessing scalar concentration footprint climatology and land surface impacts on tall-tower co 2, concentration measurements in the boreal forest of central Saskatchewan, Canada. Theor Appl Climatol 118(1–2):115–132CrossRefGoogle Scholar
- European Commission (2009) Joint Research Centre/Netherlands Environmental Assessment Agency, Emission Database for Global Atmospheric Research (EDGAR), release version 4.0. European Environment Agency (EEA), DenmarkGoogle Scholar
- Gerbig C, Lin JC, Wofsy SC, Daube BC, Andrews AE, & Stephens BB, et al. (2003) Toward constraining regional-scale fluxes of CO2 with atmospheric observations over a continent: 1. observed spatial variability from airborne platforms. J Geophys Res Atmos 108(D24), −Google Scholar
- Hu C, Liu SD, Cao C et al (2017) Simulation of atmospheric CO2 concentration and source apportionment analysis in Nanjing City. Acta Sci Circumst 37(10):3862–3875 (in Chinese)Google Scholar
- Hu C, Griffis TJ, Lee X, Millet DB, Chen Z, Baker JM, Xiao K (2018) Top-down constraint on anthropogenic CO2 emissions within an agricultural-urban landscape. J Geophys Res Atmos. https://doi.org/10.1029/2017JD027881
- IPCC (2013) Climate change 2013: the physical science basis, IPCC Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press,CambridgeGoogle Scholar
- Lin JC, Gerbig C, Wofsy SC, Andrews AE, Daube BC, Davis KJ et al (2003) A near-field tool for simulating the upstream influence of atmospheric observations: the stochastic time-inverted lagrangian transport (stilt) model. J Geophys Res Atmos 108(4493):1211–1222Google Scholar
- Moore J, Jacobson AD (2015) Seasonally varying contributions to urban CO2 in the Chicago, Illinois, USA region: insights from a high-resolution CO2 concentration and δ13C record. Burlingt Mag 3(1109):000052Google Scholar
- Ogle SM, Davis K, Lauvaux T, Schuh A, Cooley D, & West TO, et al. (2015) An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO2 concentration data. Environ Res Lett, 10(3)Google Scholar
- Pataki DE, Bowling DR, Ehleringer JR (2003) Seasonal cycle of carbon dioxide and its isotopic composition in an urban atmosphere: anthropogenic and biogenic effects. J Geophys Res Atmos 108(108):3047–3049Google Scholar
- Peters W, Jacobson AR, Sweeney C, Andrews AE, Conway TJ, Masarie K, Miller JB, Bruhwiler LMP, Petron G, Hirsch AI, Worthy DEJ, van der Werf GR, Randerson JT, Wennberg PO, Krol MC, Tans PP (2007) An atmospheric perspective on north american carbon dioxide exchange: carbontracker. Proc Natl Acad Sci U S A 104(48):18925–18930CrossRefGoogle Scholar
- Turner AJ, Shusterman AA, Mcdonald BC, Teige V, Harley RA, Cohen RC (2016) Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density. Atmos Chem Phys, 16 (21):1–20Google Scholar
- Turnbull JC, Sweeney C, Karion A, Newberger T, Lehman SJ, Tans PP et al (2015) Toward quantification and source sector identification of fossil fuel CO2, emissions from an urban area: results from the influx experiment. J Geophys Res Atmos 120(1, 292):–312Google Scholar