2002-2017 anthropogenic emissions data for air quality modeling over the United States

The United States Environmental Protection Agency (US EPA) has developed a set of annual North American emissions data for multiple air pollutants across 18 broad source categories for 2002 through 2017. The sixteen new annual emissions inventories were developed using consistent input data and methods across all years. When a consistent method or tool was not available for a source category, emissions were estimated by scaling data from the EPA's 2017 National Emissions Inventory with scaling factors based on activity data and/or emissions control information. The emissions datasets are designed to support regional air quality modeling for a wide variety of human health and ecological applications. The data were developed to support simulations of the EPA's Community Multiscale Air Quality model but can also be used by other regional scale air quality models. The emissions data are one component of EPA's Air Quality Time Series Project which also includes air quality modeling inputs (meteorology, initial conditions, boundary conditions) and outputs (e.g., ozone, PM_2.5 and constituent species, wet and dry deposition) for the Conterminous US at a 12 km horizontal grid spacing.

Improved Estimation of Trends in U.S. Ozone Concentrations Adjusted for Interannual Variability in Meteorological Conditions

Daily maximum 8-hour average (MDA8) ozone (O₃) concentrations are well-known to be influenced by local meteorological conditions, which vary across both daily and seasonal temporal scales. Previous studies have adjusted long-term trends in O₃ concentrations for meteorological effects using various statistical and mathematical methods in order to get a better estimate of the long-term changes in O₃ concentrations due to changes in precursor emissions such as nitrogen oxides (NO_X) and volatile organic compounds (VOCs). In this work, the authors present improvements to the current method used by the United States Environmental Protection Agency (US EPA) to adjust O₃ trends for meteorological influences by making refinements to the input data sources and by allowing the underlying statistical model to vary locally using a variable selection procedure. The current method is also expanded by using a quantile regression model to adjust trends in the 90^th and 98^th percentiles of the distribution of MDA8 O₃ concentrations, allowing for a better understanding of the effects of local meteorology on peak O₃ levels in addition to seasonal average concentrations. The revised method is used to adjust trends in the May to September mean, 90^th percentile, and 98^th percentile MDA8 O₃ concentrations at over 700 monitoring sites in the U.S. for years 2000 to 2016. The utilization of variable selection and quantile regression allow for a more in-depth understanding of how weather conditions affect O₃ levels in the U.S. This represents a fundamental advancement in our ability to understand how interannual variability in weather conditions in the U.S. may impact attainment of the O₃ National Ambient Air Quality Standards (NAAQS).

Characterizing CO and NO y Sources and Relative Ambient Ratios in the Baltimore Area Using Ambient Measurements and Source Attribution Modeling

Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess source contributions and evaluate the utility of using aircraft measured CO and NO _y relationships to constrain emission inventories. We derive ambient and modeled ΔCO:ΔNO _y ratios that have previously been interpreted to represent CO:NO _y ratios in emissions from local sources. Modeled and measured ΔCO:ΔNO _y are similar; however, measured ΔCO:ΔNO _y has much more daily variability than modeled values. Sector-based tagging shows that regional transport, on-road gasoline vehicles, and nonroad equipment are the major contributors to modeled CO mixing ratios in the Baltimore area. In addition to those sources, on-road diesel vehicles, soil emissions, and power plants also contribute substantially to modeled NO _y in the area. The sector mix is important because emitted CO:NO _x ratios vary by several orders of magnitude among the emission sources. The model-predicted gasoline/diesel split remains constant across all measurement locations in this study. Comparison of ΔCO:ΔNO _y to emitted CO:NO _y is challenged by ambient and modeled evidence that free tropospheric entrainment, and atmospheric processing elevates ambient ΔCO:ΔNO _y above emitted ratios. Specifically, modeled ΔCO:ΔNO _y from tagged mobile source emissions is enhanced 5-50% above the emitted ratios at times and locations of aircraft measurements. We also find a correlation between ambient formaldehyde concentrations and measured ΔCO:ΔNO _y suggesting that secondary CO formation plays a role in these elevated ratios. This analysis suggests that ambient urban daytime ΔCO:ΔNO _y values are not reflective of emitted ratios from individual sources.