Christopher W. Tessum

Life cycle air quality impacts of conventional and alternative light-duty transportation in the United States

Significance Our assessment of the life cycle air quality impacts on human health of 10 alternatives to conventional gasoline vehicles finds that electric vehicles (EVs) powered by electricity from natural gas or wind, water, or solar power are best for improving air quality, whereas vehicles powered by corn ethanol and EVs powered by coal are the worst. This work advances the current debate over the environmental impacts of conventional versus alternative transportation options by combining detailed spatially and temporally explicit emissions inventories with state-of-the-science air quality impact analysis using advanced chemical transport modeling. Our results reinforce previous findings that air quality-related health damages from transportation are generally comparable to or larger than climate change-related damages. Commonly considered strategies for reducing the environmental impact of light-duty transportation include using alternative fuels and improving vehicle fuel economy. We evaluate the air quality-related human health impacts of 10 such options, including the use of liquid biofuels, diesel, and compressed natural gas (CNG) in internal combustion engines; the use of electricity from a range of conventional and renewable sources to power electric vehicles (EVs); and the use of hybrid EV technology. Our approach combines spatially, temporally, and chemically detailed life cycle emission inventories; comprehensive, fine-scale state-of-the-science chemical transport modeling; and exposure, concentration–response, and economic health impact modeling for ozone (O3) and fine particulate matter (PM2.5). We find that powering vehicles with corn ethanol or with coal-based or “grid average” electricity increases monetized environmental health impacts by 80% or more relative to using conventional gasoline. Conversely, EVs powered by low-emitting electricity from natural gas, wind, water, or solar power reduce environmental health impacts by 50% or more. Consideration of potential climate change impacts alongside the human health outcomes described here further reinforces the environmental preferability of EVs powered by low-emitting electricity relative to gasoline vehicles.

Emissions of C6–C8 aromatic compounds in the United States: Constraints from tall tower and aircraft measurements

We present two full years of continuous C6–C8 aromatic compound measurements by PTR-MS at the KCMP tall tower (Minnesota, US) and employ GEOS-Chem nested grid simulations in a Bayesian inversion to interpret the data in terms of new constraints on US aromatic emissions. Based on the tall tower data, we find that the RETRO inventory (year-2000) overestimates US C6–C8 aromatic emissions by factors of 2.0–4.5 during 2010–2011, likely due in part to post-2000 reductions. Likewise, our implementation of the US EPAs NEI08 overestimates the toluene flux by threefold, reflecting an inventory bias in non-road emissions plus uncertainties associated with species lumping. Our annual top-down emission estimates for benzene and C8 aromatics agree with the NEI08 bottom-up values, as does the inferred contribution from non-road sources. However, the NEI08 appears to underestimate on-road emissions of these compounds by twofold during the warm season. The implied aromatic sources upwind of North America are more than double the prior estimates, suggesting a substantial underestimate of East Asian emissions, or large increases there since 2000. Long-range transport exerts an important influence on ambient benzene over the US: on average 43% of its wintertime abundance in the US Upper Midwest is due to sources outside North America. Independent aircraft measurements show that the inventory biases found here for C6–C8 aromatics also apply to other parts of the US, with notable exceptions for toluene in California and Houston, Texas. Our best estimates of year-2011 contiguous US emissions are 206 (benzene), 408 (toluene), and 822 (C8 aromatics) GgC.

Natural and anthropogenic ethanol sources in North America and potential atmospheric impacts of ethanol fuel use

We used an ensemble of aircraft measurements with the GEOS-Chem chemical transport model to constrain present-day North American ethanol sources, and gauge potential long-range impacts of increased ethanol fuel use. We find that current ethanol emissions are underestimated by 50% in Western North America, and overestimated by a factor of 2 in the east. Our best estimate for year-2005 North American ethanol emissions is 670 GgC/y, with 440 GgC/y from the continental U.S. We apply these optimized source estimates to investigate two scenarios for increased ethanol fuel use in the U.S.: one that assumes a complete transition from gasoline to E85 fuel, and one tied to the biofuel requirements of the U.S. Energy Indepence and Security Act (EISA). For both scenarios, increased ethanol emissions lead to higher atmospheric acetaldehyde concentrations (by up to 14% during winter for the All-E85 scenario and 2% for the EISA scenario) and an associated shift in reactive nitrogen partitioning reflected by an increase in the peroxyacetyl nitrate (PAN) to NO(y) ratio. The largest relative impacts occur during fall, winter, and spring because of large natural emissions of ethanol and other organic compounds during summer. Projected changes in atmospheric PAN reflect a balance between an increased supply of peroxyacetyl radicals from acetaldehyde oxidation, and the lower NO(x) emissions for E85 relative to gasoline vehicles. The net effect is a general PAN increase in fall through spring, and a weak decrease over the U.S. Southeast and the Atlantic Ocean during summer. Predicted NO(x) concentrations decrease in surface air over North America (by as much 5% in the All-E85 scenario). Downwind of North America this effect is counteracted by higher NO(x) export efficiency driven by increased PAN production and transport. From the point of view of NO(x) export from North America, the increased PAN formation associated with E85 fuel use thus acts to offset the associated lower NO(x) emissions.

The social costs of nitrogen

Nitrogen negatively affects health, climate, and water quality with costs that vary across space. Despite growing recognition of the negative externalities associated with reactive nitrogen (N), the damage costs of N to air, water, and climate remain largely unquantified. We propose a comprehensive approach for estimating the social cost of nitrogen (SCN), defined as the present value of the monetary damages caused by an incremental increase in N. This framework advances N accounting by considering how each form of N causes damages at specific locations as it cascades through the environment. We apply the approach to an empirical example that estimates the SCN for N applied as fertilizer. We track impacts of N through its transformation into atmospheric and aquatic pools and estimate the distribution of associated costs to affected populations. Our results confirm that there is no uniform SCN. Instead, changes in N management will result in different N-related costs depending on where N moves and the location, vulnerability, and preferences of populations affected by N. For example, we found that the SCN per kilogram of N fertilizer applied in Minnesota ranges over several orders of magnitude, from less than

InMAP: A model for air pollution interventions

0.001/kg N to greater than

Data and visualizations of air quality impacts of conventional and alternative light-duty transportation in the United States

10/kg N, illustrating the importance of considering the site, the form of N, and end points of interest rather than assuming a uniform cost for damages. Our approach for estimating the SCN demonstrates the potential of integrated biophysical and economic models to illuminate the costs and benefits of N and inform more strategic and efficient N management.

Reply to Oron: Electric vehicles provide an opportunity to reduce environmental health effects of transportation

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of −17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.

A Spatially and Temporally Explicit Life Cycle Inventory of Air Pollutants from Gasoline and Ethanol in the United States

The repository includes five main files: DatasetS1.xls: A directory of Microsoft Excel files containing emissions amounts disaggregated by life cycle stage for each scenario. DatasetS2.pdf: Maps of annual average ground level concentrations of PM2.5, O3, PM10, NOx, HCHO, NH3, particulate SO4, particulate NH4, particulate NO3, organic aerosol, elemental carbon aerosol, particle number, and CO; maps of annual average daily peak O3 concentrations; and maps of PM2.5 and O3 concentrations animated by month of year, day of week, and hour of day for the baseline simulation and each scenario. A pdf viewer that allows embedded javascript, such as Adobe Acrobat, is required to view the animations. VideoS1.mp4: A video showing temporal variation in PM2.5 concentrations attributable to each scenario. VideoS2.mp4: A video showing temporal variation in O3 concentrations attributable to each scenario. PublicationFigures.pdf: A PDF containing Figures 2 and 3, along with accompanying data, from the related publication (dx.doi.org/10.1073/pnas.1406853111).

Effect of increasing urban albedo on meteorology and air quality of Montreal (Canada) – Episodic simulation of heat wave in 2005

Oron (1) argues that our study (2) uses “inappropriate” methods and is framed in a way that leads “readers toward misguided conclusions.” Both of these arguments are misplaced and seem more focused on some media coverage of our article than on our article itself. Oron’s (1) specific critiques do not call into question any of the main conclusions of our report (2).