Air quality impacts of distributed power generation in the South Coast Air Basin of California 2: Model uncertainty and sensitivity analysis

摘要: Abstract Uncertainty and sensitivity of ozone PM 2.5 aerosol to variations in selected input parameters are investigated with a Monte Carlo methodology using three-dimensional air quality model. The selection is based on their potential affect concentration levels predicted by the model reflect changes emissions due implementation distributed generation (DG) South Coast Air Basin (SoCAB) California. Numerical simulations performed CIT Response predictions variation separate impacts DG from uncertainty. This study provides measure errors for species concentrations. A spatial analysis used investigate effect placing specific regions SoCAB. In general, results show that confidence greatest locations where concentrations highest. Changes no greater than 80% nominal values variables, cause 18% mixing ratios 25% Sensitivity reveals nitrogen oxides ( NO x ) side boundary conditions volatile organic compounds (VOC) major contributors uncertainty predictions. An increase leads reductions at peak times sites maximum located. most sensitive NH 3 emissions. Increasing these higher analyses highly dependent both space time. particular, reduced during nighttime nearby DGs installed. However, daytime downwind sources. finding this installed coastal areas produce significant impact production eastern