Estimates of ozone response to various combinations of NO(x) and VOC emission reductions in the eastern United States

There is increasing recognition that controls on NO(x) emissions may be necessary, in addition to existing and future Volatile Organic Compounds (VOC) controls, for the abatement of ozone (O3) over portions of the United States. This study compares various combinations of anthropogenic NO(x) and VOC emission reductions through a series of model simulations. A total of 6 simulations were performed with the Regional Oxidant Model (ROM) for a 9-day period in July 1988. Each simulation reduced anthropogenic NO(x) and VOC emissions across-the-board by different amounts. Maximum O3 concentrations for the period were compared between the simulations. Comparison of the simulations suggests that: (1) NO(x) controls may be more effective than VOC controls in reducing peak O3 over most of the eastern United States; (2) VOC controls are most effective in urban areas having large sources of emissions; (3) NO(x) controls may increase O3 near large point sources; and (4) the benefit gained from increasing the amount of VOC controls may lessen as the amount of NO(x) control is increased. This paper has been reviewed in accordance with the U.S. Environmental Protection Agency's peer and administrative review policies and approved for presentation and publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use

Assimilation of Satellite Data in Regional Air Quality Models

In terms of important uncertainty in regional-scale air-pollution models, probably no other aspect ranks any higher than the current ability to specify clouds and soil moisture on the regional scale. Because clouds in models are highly parameterized, the ability of models to predict the correct spatial and radiative characteristics is highly suspect and subject to large error. The poor representation of cloud fields from point measurements at National Weather Services stations and the almost total absence of surface moisture availability observations has made assimilation of these variables difficult to impossible. Yet, the correct inclusion of clouds and surface moisture are of first-order importance in regional-scale photochemistry

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

Copyright 2011 Elsevier B.V., All rights reserved.Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 "cut-outs" from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care.Peer reviewedFinal Accepted Versio

Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations

This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared to when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatio-temporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intra-day time scale over both NA and EU. Furthermore, we introduce a methodology to distinguish between locally-influenced and regionally-representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally-influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems’ ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.JRC.C.5-Air and Climat