Abstract
Uncertainty and sensitivity of ozone and PM 2.5 aerosol to variations in selected input parameters are investigated with a Monte Carlo methodology using a three-dimensional air quality model. The selection of input parameters is based on their potential to affect concentration levels of ozone and PM 2.5 predicted by the model and to reflect changes in emissions due to the implementation of distributed generation (DG) in the South Coast Air Basin (SoCAB) of California. Numerical simulations are performed with the CIT air quality model. Response of the CIT predictions to the variation of selected input parameters is investigated to separate the potential air quality impacts of DG from model uncertainty. This study provides a measure of the model errors for selected species concentrations. A spatial sensitivity analysis is used to investigate the effect of placing DG in specific regions of the SoCAB. In general, results show that confidence in the model results is greatest in locations where ozone and PM 2.5 concentrations are the highest. Changes no greater than 80% in the nominal values of selected input variables, cause changes of 18% in ozone mixing ratios and 25% for PM 2.5 aerosol concentrations. Sensitivity analysis reveals that nitrogen oxides ( NO x ) emissions and side boundary conditions of volatile organic compounds (VOC) are the major contributors to uncertainty and sensitivity of ozone predictions. An increase in NO x emissions leads to reductions in ozone mixing ratios at peak times and sites where the maximum values are located. PM 2.5 aerosol is most sensitive to changes in NH 3 and NO x emissions. Increasing these emissions leads to higher aerosol concentrations. Sensitivity analyses show that the impacts of DG implementation are highly dependent on both space and time. In particular, ozone concentrations are reduced during the nighttime nearby locations where DGs are installed. However, during the daytime ozone concentrations increase downwind from the sources. A major finding of this study is that the emissions of DG installed in coastal areas produce a significant impact on the production of ozone and PM 2.5 aerosol in the eastern regions of the SoCAB.
Highlights
Distributed energy resources (DER) have the potential to provide a considerable portion of theM.A
Most sites show that volatile organic compounds (VOC) and ozone boundary conditions have an important effect on ozone concentrations
Some of these results reflect the proximity to the computational boundaries, like Simi Valley, and the smaller amount of nitrogen oxides (NOx) emission sources in that region
Summary
Distributed energy resources (DER) have the potential to provide a considerable portion of the. Monte Carlo analyses have been used extensively in regional-scale gas-phase mechanisms to address uncertainty assessment (Derwent and Hov, 1988; Gao et al, 1996; Phenix et al, 1998; Bergin et al, 1999; Grenfell et al, 1999; Hanna et al, 2001; Vuilleumier et al, 2001) This manuscript presents the first study in which unique aspects are considered to include the impacts of DG implementation in the uncertainty and sensitivity analysis of a three-dimensional air quality model. This work examines the response of specific air quality model predictions in order to separate the DG air quality impacts from model uncertainties It provides a measure of the error bounds for simulated concentrations of ozone and particulate matter less than 2:5 mm (PM2:5). The most innovative contributions are the characterization of the spatial variation of the model’s errors to determine those areas in the SoCAB where the predictions display the largest uncertainties and the systematic development of scenarios for a thorough spatial sensitivity analysis that investigates the effects of placing DG in specific regions of the SoCAB
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