ESP v2.0: enhanced method for exploring emission impacts of future scenarios in the United States – addressing spatial allocation

L Ran,D H Loughlin,B H Baek,Z Adelman,D Yang,C G Nolte

doi:10.5194/gmd-8-1775-2015

L Ran, D H Loughlin + Show 4 more

Open Access

https://doi.org/10.5194/gmd-8-1775-2015

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Abstract

Abstract. The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating future-year non-power sector emissions to account for projected population and land use changes. In ESP v2.0, US Census division-level emission growth factors are developed using an energy system model. Regional factors for population-related emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multi-decadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios.

Highlights

Emission projections are often the dominant factor influencing the outcome of future-year air quality modeling studies (e.g., Tagaris et al, 2007; Tao et al, 2007; Avise et al, 2009)
Major steps in the generation of future emissions for an air quality model include the application of multiplicative emission growth and control factors to produce a futureyear emission inventory, temporal allocation of emissions by season, day and hour, and spatial allocation of hourly emissions onto a 2-dimensional grid over the modeling domain
It is commonplace in such applications to apply these growth factors such that emissions grow in place

Summary

Introduction

Emission projections are often the dominant factor influencing the outcome of future-year air quality modeling studies (e.g., Tagaris et al, 2007; Tao et al, 2007; Avise et al, 2009). Multiplicative emission growth factors are developed using the MARKet ALlocation (MARKAL) energy system model (Fishbone and Abilock, 1981; Loulou et al, 2004) These factors are applied to a base-year emissions inventory, such as the United States Environmental Protection Agency (US EPA) National Emissions Inventory (NEI) (US EPA, 2010), using the Sparse Matrix Operator Kernel Emission (SMOKE) model (Houyoux et al, 2000). Major steps in the generation of future emissions for an air quality model include the application of multiplicative emission growth and control factors to produce a futureyear emission inventory, temporal allocation of emissions by season, day and hour, and spatial allocation of hourly emissions onto a 2-dimensional grid over the modeling domain. Note that the spatial surrogate shapefiles were subsequently updated in the 2011 EPA modeling platform (US EPA, 2011, 2014a, b)

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