Abstract
We assess the detectability of city emissions via a tower-based greenhouse gas (GHG) network, as part of the Indianapolis Flux (INFLUX) experiment. By examining afternoon-averaged results from a network of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) mole fraction measurements in Indianapolis, Indiana for 2011–2013, we quantify spatial and temporal patterns in urban atmospheric GHG dry mole fractions. The platform for these measurements is twelve communications towers spread across the metropolitan region, ranging in height from 39 to 136 m above ground level, and instrumented with cavity ring-down spectrometers. Nine of the sites were deployed as of January 2013 and data from these sites are the focus of this paper. A background site, chosen such that it is on the predominantly upwind side of the city, is utilized to quantify enhancements caused by urban emissions. Afternoon averaged mole fractions are studied because this is the time of day during which the height of the boundary layer is most steady in time and the area that influences the tower measurements is likely to be largest. Additionally, atmospheric transport models have better performance in simulating the daytime convective boundary layer compared to the nighttime boundary layer. Averaged from January through April of 2013, the mean urban dormant-season enhancements range from 0.3 ppm CO2 at the site 24 km typically downwind of the edge of the city (Site 09) to 1.4 ppm at the site at the downwind edge of the city (Site 02) to 2.9 ppm at the downtown site (Site 03). When the wind is aligned such that the sites are downwind of the urban area, the enhancements are increased, to 1.6 ppm at Site 09, and 3.3 ppm at Site 02. Differences in sampling height affect the reported urban enhancement by up to 50%, but the overall spatial pattern remains similar. The time interval over which the afternoon data are averaged alters the calculated urban enhancement by an average of 0.4 ppm. The CO2 observations are compared to CO2 mole fractions simulated using a mesoscale atmospheric model and an emissions inventory for Indianapolis. The observed and modeled CO2 enhancements are highly correlated (r2 = 0.94), but the modeled enhancements prior to inversion average 53% of those measured at the towers. Following the inversion, the enhancements follow the observations closely, as expected. The CH4 urban enhancement ranges from 5 ppb at the site 10 km predominantly downwind of the city (Site 13) to 21 ppb at the site near the landfill (Site 10), and for CO ranges from 6 ppb at the site 24 km downwind of the edge of the city (Site 09) to 29 ppb at the downtown site (Site 03). Overall, these observations show that a dense network of urban GHG measurements yield a detectable urban signal, well-suited as input to an urban inversion system given appropriate attention to sampling time, sampling altitude and quantification of background conditions.
Highlights
Atmospheric greenhouse gas (GHG) mole fractions continue to rise rapidly, primarily in response to anthropogenic emissions from fossil fuel consumption (IPCC 2014)
This study provides a description of multi-species variability of atmospheric GHGs along with the high spatial and temporal resolution we expect to be needed to fully characterize and quantify urban emissions across space, time, and economic sectors in a large metropolitan area
3 Results 3.1 Background sites we evaluate the suitability of Site 01 and Site 09 as background sites by considering the difference between the CO2 mole fraction measured at each site for each afternoon hour and the minimum mole fraction across the Indianapolis Flux (INFLUX) tower network measured at the same hour for the period 1 January – 30 April 2013
Summary
Atmospheric greenhouse gas (GHG) mole fractions continue to rise rapidly (currently at about 2.5 ppm/year), primarily in response to anthropogenic emissions from fossil fuel consumption (IPCC 2014). A number of global products have used night lights and other remote sensing techniques to develop spatially-distributed emissions estimates (Oda and Maksyutov 2011; Rayner et al, 2010), sometimes including an uncertainty assessment (Asefi-Najafabady et al, 2014). All of these products use the same large-scale data utilized in national inventory products, but take a variety of approaches to distribute these emissions in space and time
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