Journal
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
Volume 120, Issue 1, Pages 292-312Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1002/2014JD022555
Keywords
urban greenhouse gases; fossil fuel CO2; radiocarbon
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Funding
- National Institute of Standards and Technology [60NANB10D023]
- National Oceanic and Atmospheric Administration Climate Program Office's AC4 program [NA13OAR4310074]
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The Indianapolis Flux Experiment (INFLUX) aims to develop and assess methods for quantifying urban greenhouse gas emissions. Here we use CO2, (CO2)-C-14, and CO measurements from tall towers around Indianapolis, USA, to determine urban total CO2, the fossil fuel derived CO2 component (CO(2)ff), and CO enhancements relative to background measurements. When a local background directly upwind of the urban area is used, the wintertime total CO2 enhancement over Indianapolis can be entirely explained by urban CO(2)ff emissions. Conversely, when a continental background is used, CO(2)ff enhancements are larger and account for only half the total CO2 enhancement, effectively representing the combined CO(2)ff enhancement from Indianapolis and the wider region. In summer, we find that diurnal variability in both background CO2 mole fraction and covarying vertical mixing makes it difficult to use a simple upwind-downwind difference for a reliable determination of total CO2 urban enhancement. We use characteristic CO(2)ff source sector CO:CO(2)ff emission ratios to examine the contribution of the CO(2)ff source sectors to total CO(2)ff emissions. This method is strongly sensitive to the mobile sector, which produces most CO. We show that the inventory-based emission product (bottom up) and atmospheric observations (top down) can be directly compared throughout the diurnal cycle using this ratio method. For Indianapolis, the top-down observations are consistent with the bottom-up Hestia data product emission sector patterns for most of the diurnal cycle but disagree during the nighttime hours. Further examination of both the top-down and bottom-up assumptions is needed to assess the exact cause of the discrepancy. Key Points
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