Emissions from mobile sources: improved understanding of the drivers of emissions and their spatial patterns

Emissions of greenhouse gases from the combustion of fossil fuels, in particular carbon dioxide (CO2), are a major contributor to global climate change. In the United States 28% of carbon dioxide emissions from fossil fuel combustion are produced by road vehicles. This dissertation reports the results of three studies that improve on our knowledge of the spatial and temporal distribution of vehicle CO2 emissions in the U.S. over the last 35 years. Using bottom-up data assimilation techniques we produce several new high-resolution inventories of vehicle emissions, and use these new data products to analyze the relationships between emissions, population, employment, traffic congestion, and climate change at multiple spatial and temporal scales across the U.S. We find that population density has a strong, non-linear effect on vehicle emissions, with increasing emissions in low density areas and decreasing emissions in high density areas. We identify large biases in estimates of vehicle CO2 emissions by the most commonly used national and global inventories, and highlight the susceptibility of spatially-downscaled inventories to local biases in urban areas. We also quantify emissions of several air pollutants regulated by the U.S. Environment Protection Agency, including carbon monoxide, nitrogen oxides and particulate matter, at hourly and roadway scales for the metropolitan area surrounding Boston, MA. Emissions of these pollutants show high emissions gradients across identifiable spatial hotspots, considerable diurnal and seasonal variations, and a high sensitivity to the presence or absence of heavy-duty truck traffic. We also find that the impact of traffic congestion on air pollution emissions across the region is minimal as a share of the total emissions. We show that policies that combine a reduction in the number of vehicles on the road with a focus on improving traffic speeds have greater success in reducing emissions of air pollutants and greenhouse gases than policies that focus solely on improving traffic speeds. Finally, we estimate that regional emissions of carbon monoxide will increase by 3% in 2050, but with numerous localized increases of 25-50%, due to an expected rise in mean regional temperatures due to global climate change

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

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.https://doi.org/10.1073/pnas.1421723112Published versio

Cities, traffic, and CO2: A multi-decadal assessment of trends, drivers, and scaling relationships

Version 1.0 of the Database of Road Transportation Emissions (DARTE). This dataset contains annual estimates of CO2 emissions from on-road transportation sources in the coterminous U.S. for the years 1980 through 2012. Emissions are reported on a 1 x 1 km grid

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

A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. Methods: The Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared. Results: Compared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively. Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders