RESUMEN
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.
RESUMEN
Maritime greenhouse gas emissions are projected to increase significantly by 2050, highlighting the need for reliable inventories as a first step in analyzing ship emission control policies. The impact of ship power models on marine emissions inventories has garnered little attention, with most inventories employing simple, load-factor-based models to estimate ship power consumption. The availability of more expansive ship activity data provides the opportunity to investigate the inventory impacts of adopting complex power models. Furthermore, ship parameter fields can be sparsely populated in ship registries, making gap-filling techniques and averaging processes necessary. Therefore, it is important to understand of the impact of averaged ship parameters on ship power and emission estimations. This paper examines power estimation differences between results from two complex, resistance-based and two simple, load-factor-based power models on a baseline inventory with unique ship parameters. These models are additionally analyzed according to their sensitivities toward average ship parameters. Automated Identification System (AIS) data from a fleet of commercial marine vessels operating over a 6-month period off the coast of the southwestern United States form the basis of the analysis. To assess the inventory impacts of using averaged ship parameters, fleet-level carbon dioxide (CO2) emissions are calculated using ship parameter data averaged across ship types and their subtype size classes. Each of the four ship power models are used to generate four CO2 emissions inventories, and results are compared with baseline estimates for the same sample fleet where no averaged values were used. The results suggest that a change in power model has a relatively high impact on emission estimates. They also indicate relatively little sensitivity, by all power models, to the use of ship characteristics averaged by ship and subtype. Implications: Commercial marine vessel emissions inventories were calculated using four different models for ship engine power. The calculations used 6 months of Automated Identification System (AIS) data from a sample of 248 vessels as input data. The results show that more detailed, resistance-based models tend to estimate a lower propulsive power, and thus lower emissions, for ships than traditional load-factor-based models. Additionally, it was observed that emission calculations using averaged values for physical ship parameters had a minimal impact on the resulting emissions inventories.
