On the Impact of Granularity of Space-based Urban CO2 Emissions in Urban Atmospheric Inversions: A Case Study for Indianapolis, IN.

Quantifying greenhouse gas (GHG) emissions from cities is a key challenge towards effective emissions management. An inversion analysis from the INdianapolis FLUX experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city using CO2 data collected by the dense tower network deployed across the city. However, city-level emission data, used as a priori emissions, are also a key component in the atmospheric inversion framework. Currently, fine-grained emission inventories (EIs) able to resolve GHG city emissions at high spatial resolution, are only available for few major cities across the globe. Following the INFLUX inversion case with a global 1×1 km ODIAC fossil fuel CO2 emission dataset, we further improved the ODIAC emission field and examined its utility as a prior for the city scale inversion. We disaggregated the 1×1 km ODIAC non-point source emissions using geospatial datasets such as the global road network data and satellite-data driven surface imperviousness data to a 30×30 m resolution. We assessed the impact of the improved emission field on the inversion result, relative to priors in previous studies (Hestia and ODIAC). The posterior total emission estimate (5.1 MtC/yr) remains statistically similar to the previous estimate with ODIAC (5.3 MtC/yr). However, the distribution of the flux corrections was very close to those of Hestia inversion and the model-observation mismatches were significantly reduced both in forward and inverse runs, even without hourly temporal changes in emissions. EIs reported by cities often do not have estimates of spatial extents. Thus, emission disaggregation is a required step when verifying those reported emissions using atmospheric models. Our approach offers gridded emission estimates for global cities that could serves as a prior for inversion, even without locally reported EIs in a systematic way to support city-level Measuring, Reporting and Verification (MRV) practice implementation.

[1]  Stephen W. Pacala,et al.  Verifying greenhouse gas emissions: Methods to support international climate agreements , 2010 .

[2]  Riley M. Duren,et al.  Measuring the carbon emissions of megacities , 2012 .

[3]  Bedrich Benes,et al.  Quantification of fossil fuel CO2 emissions on the building/street scale for a large U.S. city. , 2012, Environmental science & technology.

[4]  D. Higdon,et al.  Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example , 2016 .

[5]  Kevin R. Gurney,et al.  A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of results , 2014 .

[6]  G. Janssens-Maenhout,et al.  Verifying Greenhouse Gas Emissions: Methods to Support International Climate Agreements , 2011 .

[7]  Gregg Marland,et al.  Uncertainties in Accounting for CO2 From Fossil Fuels , 2008 .

[8]  Greet Janssens-Maenhout,et al.  EDGAR-HTAP: A harmonized gridded air pollution emission dataset based on national inventories , 2012 .

[9]  K. Davis,et al.  High‐resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX) , 2016, Journal of geophysical research. Atmospheres : JGR.

[10]  Shamil Maksyutov,et al.  A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights , 2011 .

[11]  Dylan B. A. Jones,et al.  Modeling global atmospheric CO 2 with improved emission inventories and CO 2 production from the oxidation of other carbon species , 2010 .

[12]  Roger Symon,et al.  Reconciling the differences. , 1975 .

[13]  Conor K. Gately,et al.  Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships , 2015, Proceedings of the National Academy of Sciences.

[14]  T. Lauvaux,et al.  Reconciling the differences between a bottom-up and inverse-estimated FFCO 2 emissions estimate in a large US urban area , 2017 .

[15]  Lynne Milgram,et al.  Top Down versus Bottom Up , 1999 .

[16]  Philippe Peylin,et al.  A new global gridded data set of CO2 emissions from fossil fuel combustion: Methodology and evaluation , 2010 .

[17]  J. Olivier.  On the Quality of Global Emission Inventories : Approaches, Methodologies, Input Data and Uncertainties , 2002 .

[18]  Euan G. Nisbet,et al.  Top-Down Versus Bottom-Up , 2010, Science.

[19]  G. Marland,et al.  Monthly, global emissions of carbon dioxide from fossil fuel consumption , 2011 .

[20]  Michael Q. Wang,et al.  An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 , 2003 .

[21]  H. Johnson,et al.  Emissions to the Air , 2016 .

[22]  P. Ciais,et al.  Reduced carbon emission estimates from fossil fuel combustion and cement production in China , 2015, Nature.

[23]  Greet Janssens-Maenhout,et al.  Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2 , 2013 .

[24]  Makoto Saito,et al.  Comparing GOSAT observations of localized CO2 enhancements by large emitters with inventory‐based estimates , 2016 .

[25]  G. Marland.  Uncertainties in Accounting for CO 2 From Fossil Fuels , 2008 .

[26]  Annmarie Eldering,et al.  The OCO-3 Mission : Overview of Science Objectives and Status , 2015 .

[27]  Eleanor C. Stokes,et al.  Holidays in lights: Tracking cultural patterns in demand for energy services , 2015, Earth's future.

[28]  A.J.H. Visschedijk,et al.  TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling , 2014 .

[29]  Hartmut Boesch,et al.  Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO 2 and CH 4 retrieval errors by error parameterization , 2013 .

[30]  Eric A. Kort,et al.  Space‐based observations of megacity carbon dioxide , 2012 .

[31]  Michael Buchwitz,et al.  A remote sensing technique for global monitoring of power plant CO 2 emissions from space and related applications , 2010 .

[32]  G. Marland,et al.  A spatial uncertainty metric for anthropogenic CO2 emissions , 2014 .

[33]  C. Elvidge,et al.  Why VIIRS data are superior to DMSP for mapping nighttime lights , 2013 .

[34]  Kevin R. Gurney,et al.  Urbanization and the carbon cycle: Current capabilities and research outlook from the natural sciences perspective , 2014 .

[35]  Kevin R. Gurney,et al.  Toward quantification and source sector identification of fossil fuel CO2 emissions from an urban area: Results from the INFLUX experiment , 2015 .

[36]  T. Lauvaux,et al.  Los Angeles megacity: A high-resolution land-atmosphere modelling system for urban CO2 emissions , 2016 .

[37]  Gerald Mills,et al.  Cities as agents of global change , 2007 .

[38]  Christopher W. O'Dell,et al.  Performance of a geostationary mission, geoCARB, to measure CO 2 , CH 4 and CO column-averaged concentrations , 2013 .

[39]  Yong Geng,et al.  The gigatonne gap in China’s carbon dioxide inventories , 2012 .

[40]  Jeroen A. H. W. Peters,et al.  Recent trends in global greenhouse gas emissions:regional trends 1970–2000 and spatial distributionof key sources in 2000 , 2005 .

[41]  P. Ciais,et al.  A synthesis of carbon dioxide emissions from fossil-fuel combustion , 2012 .

[42]  K. Davis,et al.  Toward reduced transport errors in a high resolution urban CO2 inversion system , 2017 .