Conor K. Gately

Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships

Significance We use roadway-level traffic data to construct a 33-year, high-resolution inventory of annual on-road CO2 emissions for the United States that differs markedly from other emissions estimates. We find a highly nonlinear relationship between population density and emissions, and identify large biases in regional estimates of CO2 from inventories that rely on population as a linear predictor of vehicle activity. Geographic differences in the density–emissions relationship suggest that “smart growth” policies to increase urban residential densities will have significantly different effects on emissions depending on local conditions, and may be most effective at low densities. Our results highlight the importance of cities as sources of CO2 and the need for improved fine-scale inventories for monitoring and reporting of emissions. Emissions of CO2 from road vehicles were 1.57 billion metric tons in 2012, accounting for 28% of US fossil fuel CO2 emissions, but the spatial distributions of these emissions are highly uncertain. We develop a new emissions inventory, the Database of Road Transportation Emissions (DARTE), which estimates CO2 emitted by US road transport at a resolution of 1 km annually for 1980–2012. DARTE reveals that urban areas are responsible for 80% of on-road emissions growth since 1980 and for 63% of total 2012 emissions. We observe nonlinearities between CO2 emissions and population density at broad spatial/temporal scales, with total on-road CO2 increasing nonlinearly with population density, rapidly up to 1,650 persons per square kilometer and slowly thereafter. Per capita emissions decline as density rises, but at markedly varying rates depending on existing densities. We make use of DARTE’s bottom-up construction to highlight the biases associated with the common practice of using population as a linear proxy for disaggregating national- or state-scale emissions. Comparing DARTE with existing downscaled inventories, we find biases of 100% or more in the spatial distribution of urban and rural emissions, largely driven by mismatches between inventory downscaling proxies and the actual spatial patterns of vehicle activity at urban scales. Given cities’ dual importance as sources of CO2 and an emerging nexus of climate mitigation initiatives, high-resolution estimates such as DARTE are critical both for accurately quantifying surface carbon fluxes and for verifying the effectiveness of emissions mitigation efforts at urban scales.

A bottom up approach to on-road CO2 emissions estimates: improved spatial accuracy and applications for regional planning.

On-road transportation is responsible for 28% of all U.S. fossil-fuel CO2 emissions. Mapping vehicle emissions at regional scales is challenging due to data limitations. Existing emission inventories use spatial proxies such as population and road density to downscale national or state-level data. Such procedures introduce errors where the proxy variables and actual emissions are weakly correlated, and limit analysis of the relationship between emissions and demographic trends at local scales. We develop an on-road emission inventory product for Massachusetts-based on roadway-level traffic data obtained from the Highway Performance Monitoring System (HPMS). We provide annual estimates of on-road CO2 emissions at a 1 × 1 km grid scale for the years 1980 through 2008. We compared our results with on-road emissions estimates from the Emissions Database for Global Atmospheric Research (EDGAR), with the Vulcan Product, and with estimates derived from state fuel consumption statistics reported by the Federal Highway Administration (FHWA). Our model differs from FHWA estimates by less than 8.5% on average, and is within 5% of Vulcan estimates. We found that EDGAR estimates systematically exceed FHWA by an average of 22.8%. Panel regression analysis of per-mile CO2 emissions on population density at the town scale shows a statistically significant correlation that varies systematically in sign and magnitude as population density increases. Population density has a positive correlation with per-mile CO2 emissions for densities below 2000 persons km(-2), above which increasing density correlates negatively with per-mile emissions.

Modeling and validation of on-road CO2 emissions inventories at the urban regional scale

On-road emissions are a major contributor to rising concentrations of atmospheric greenhouse gases. In this study, we applied a downscaling methodology based on commonly available spatial parameters to model on-road CO(2) emissions at the 1 × 1 km scale for the Boston, MA region and tested our approach with surface-level CO(2) observations. Using two previously constructed emissions inventories with differing spatial patterns and underlying data sources, we developed regression models based on impervious surface area and volume-weighted road density that could be scaled to any resolution. We found that the models accurately reflected the inventories at their original scales (R(2) = 0.63 for both models) and exhibited a strong relationship with observed CO(2) mixing ratios when downscaled across the region. Moreover, the improved spatial agreement of the models over the original inventories confirmed that either product represents a viable basis for downscaling in other metropolitan regions, even with limited data.

Variability, drivers, and effects of atmospheric nitrogen inputs across an urban area: Emerging patterns among human activities, the atmosphere, and soils

Atmospheric deposition of nitrogen (N) is a major input of N to the biosphere and is elevated beyond preindustrial levels throughout many ecosystems. Deposition monitoring networks in the United States generally avoid urban areas in order to capture regional patterns of N deposition, and studies measuring N deposition in cities usually include only one or two urban sites in an urban-rural comparison or as an anchor along an urban-to-rural gradient. Describing patterns and drivers of atmospheric N inputs is crucial for understanding the effects of N deposition; however, little is known about the variability and drivers of atmospheric N inputs or their effects on soil biogeochemistry within urban ecosystems. We measured rates of canopy throughfall N as a measure of atmospheric N inputs, as well as soil net N mineralization and nitrification, soil solution N, and soil respiration at 15 sites across the greater Boston, Massachusetts area. Rates of throughfall N are 8.70±0.68kgNha-1yr-1, vary 3.5-fold across sites, and are positively correlated with rates of local vehicle N emissions. Ammonium (NH4+) composes 69.9±2.2% of inorganic throughfall N inputs and is highest in late spring, suggesting a contribution from local fertilizer inputs. Soil solution NO3- is positively correlated with throughfall NO3- inputs. In contrast, soil solution NH4+, net N mineralization, nitrification, and soil respiration are not correlated with rates of throughfall N inputs. Rather, these processes are correlated with soil properties such as soil organic matter. Our results demonstrate high variability in rates of urban throughfall N inputs, correlation of throughfall N inputs with local vehicle N emissions, and a decoupling of urban soil biogeochemistry and throughfall N inputs.

Large Uncertainties in Urban‐Scale Carbon Emissions

Accurate estimates of fossil fuel carbon dioxide (FFCO2) emissions are a critical component of local, regional, and global climate agreements. Current global inventories of FFCO2 emissions do not directly quantify emissions at local scales, instead spatial proxies like population density, nighttime lights, and powerplant databases are used to downscale emissions from national totals. We have developed a high-resolution (hourly, 1km2) bottom-up Anthropogenic Carbon Emissions System (ACES) for FFCO2, based on local activity data for the year 2011 across the Northeastern U.S. We compare ACES with three widely used global inventories, finding significant differences at regional (20%) and city scales (50-250%). At a spatial resolution of 0.1°, inventories differ by over 100% for half of the grid cells in the domain, with the largest differences in urban areas and oil and gas production regions. Given recent US federal policy pull-backs regarding greenhouse gas emissions reductions, inventories like ACES are crucial for US actions, as the impetus for climate leadership has shifted to city and state governments. The development of a robust carbon monitoring system to track carbon fluxes is critical for emissions benchmarking and verification. We show that existing downscaled inventories are not suitable for urban emissions monitoring, as they do not consider important local activity patterns. The ACES methodology is designed for easy updating, making it suitable for emissions monitoring under most city, regional, and state greenhouse gas mitigation initiatives, in particular for the small and medium-sized cities that lack the resources to regularly perform their own bottom-up emissions inventories.

Anthropogenic and biogenic CO2 fluxes in the Boston urban region

Significance Cities are taking a leading role in US efforts to reduce greenhouse gas emissions, and require traceable methods to assess the efficacy of their efforts. In this study, we developed an inverse model framework that quantified emissions in the Boston urban region over 16 months and is capable of detecting changes in emissions of greater than 18%. We show that a detailed representation of urban biological fluxes and knowledge of the spatial and temporal distribution of emissions are essential for accurate modeling of annual CO2 emissions. Across the globe, it is possible to quantifiably assess the efficacy of mitigation efforts by developing frameworks similar to the one we present here for Boston. With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO2 from five sites in and around Boston were combined with a high-resolution bottom-up CO2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model−measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO2 fluxes, and development of a CO2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m−2·y−1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model−measurement approach demonstrated here, our framework should be able to detect changes in CO2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.