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, PM2.5 and constituent species, wet and dry deposition) for the Conterminous US at a 12 km horizontal grid spacing.

Evaluation of 15 years of modeled atmospheric oxidized nitrogen compounds across the contiguous United States.

Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NO X ) estimates from annual photochemical simulations for years 2002-2016 are compared to surface network measurements of NO X and total gas-phase-oxidized reactive nitrogen (NO Y ) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NO X performance. Summertime NO X is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NO X bias dropped from between 28% and 57% for earlier years (2002-2012) to between -2% and 7% for later years (2013-2016). Summer daytime NO X tends to be unbiased or underpredicted. In winter, the evening NO X overpredictions remain, but NO X is unbiased or underpredicted overnight, in the morning, and during the day. NO X overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NO X . Modeled NO X performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NO X emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NO Y but do not improve summertime NO X bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NO X performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.

Contribution of mobile sources to secondary formation of carbonyl compounds.

In the 2014 National Air Toxics Assessment (NATA), the carbonyl compounds formaldehyde and acetaldehyde were identified as key cancer risk drivers and acrolein was identified as one of the three air toxics that drive most of the noncancer risk. In this assessment, averaged across the Continental United States, about 75% of ambient formaldehyde and acetaldehyde, and about 18% of acrolein, is formed secondarily. This study was conducted to estimate the potential contribution to these secondarily formed carbonyl compounds from mobile sources. To develop such estimates, we conducted several CMAQ runs, where emissions are set to zero for different mobile source sectors, to determine their potential contribution. Although zeroing out emissions from an individual sector can offer only a rough approximation of how the sector might contribute to overall secondary concentrations, our results suggest that across the U. S., mobile sources contribute about 6-18% to secondary formaldehyde, 0-10% to secondary acetaldehyde, and 0-70% to secondary acrolein, depending on location. Implications: Photochemical modeling of carbonyl compounds was conducted with emissions set to zero for various mobile source sectors to determine their contribution to secondary concentrations. Results indicated mobile sources contributed to total and secondary concentrations of formaldehyde, acetaldehyde, and acrolein in many locations across the U.S. with acrolein the dominant contributor in some locations. However, biogenic sources dominated secondary formaldehyde and acetaldehyde, and fires dominated secondary acrolein.

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.

Assessing Human Health PM2.5 and Ozone Impacts from U.S. Oil and Natural Gas Sector Emissions in 2025.

Incomplete information regarding emissions from oil and natural gas production has historically made it challenging to characterize the air quality or air pollution-related health impacts for this sector in the United States. Using an emissions inventory for the oil and natural gas sector that reflects information regarding the level and distribution of PM2.5 and ozone precursor emissions, we simulate annual mean PM2.5 and summer season average daily 8 h maximum ozone concentrations with the Comprehensive Air-Quality Model with extensions (CAMx). We quantify the incidence and economic value of PM2.5 and ozone health related effects using the environmental Benefits Mapping and Analysis Program (BenMAP). We find that ambient concentrations of PM2.5 and ozone, and associated health impacts, are highest in a handful of states including Colorado, Pennsylvania, Texas and West Virginia. On a per-ton basis, the benefits of reducing PM2.5 precursor emissions from this sector vary by pollutant species, and range from between $6,300 and $320,000, while the value of reducing ozone precursors ranges from $500 to $8,200 in the year 2025 (2015$).

Hybrid Modeling Approach to Estimate Exposures of Hazardous Air Pollutants (HAPs) for the National Air Toxics Assessment (NATA).

A hybrid air quality model has been developed and applied to estimate annual concentrations of 40 hazardous air pollutants (HAPs) across the continental United States (CONUS) to support the 2011 calendar year National Air Toxics Assessment (NATA). By combining a chemical transport model (CTM) with a Gaussian dispersion model, both reactive and nonreactive HAPs are accommodated across local to regional spatial scales, through a multiplicative technique designed to improve mass conservation relative to previous additive methods. The broad scope of multiple pollutants capturing regional to local spatial scale patterns across a vast spatial domain is precedent setting within the air toxics community. The hybrid design exhibits improved performance relative to the stand alone CTM and dispersion model. However, model performance varies widely across pollutant categories and quantifiably definitive performance assessments are hampered by a limited observation base and challenged by the multiple physical and chemical attributes of HAPs. Formaldehyde and acetaldehyde are the dominant HAP concentration and cancer risk drivers, characterized by strong regional signals associated with naturally emitted carbonyl precursors enhanced in urban transport corridors with strong mobile source sector emissions. The multiple pollutant emission characteristics of combustion dominated source sectors creates largely similar concentration patterns across the majority of HAPs. However, reactive carbonyls exhibit significantly less spatial variability relative to nonreactive HAPs across the CONUS.