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Photochemical modeling has been used to determine the effect of fuels and vehicles on the urban ozone. Input data was lifted from the Auto/Air Quality Improvement Research Program (AQIRP). Multiday simulations were performed for gasolines in various AQIRP test matrices and for a methanol/gasoline blend. Among the gasoline changes which were recommended to reduce light-duty vehicles' role in ozone formation were olefin content and vapor pressure reductions and 90% distillation temperature.

The decoupled direct method (DDM) is an efficient and accurate way of performing sensitivity analysis to model inputs. As the impact of atmospheric particulate matter (PM) on human health and visibility became evident, the need to extend the DDM to PM sensitivity has grown. In this work, the DDM is implemented in the three PM modules employed in the Comprehensive Air-quality Model with extensions (CAMx): ISORROPIA for inorganic gas/aerosol partitioning, SOAP for secondary organic gas/aerosol partitioning, and RADM-AQ for aqueous-phase chemistry. The PM modules are complex and the DDM implementation is discussed in detail. Stand-alone tests are performed for each PM module in which first-order sensitivities computed by the DDM are compared to the traditional brute-force method (BFM). The DDM sensitivities are shown to be accurate and agree well with the BFM within the linear response range. The SOAP module showed nearly linear response for up to ±30% changes in concentration inputs. The RADM-AQ module showed moderately nonlinear response in some tests but first-order sensitivities accounted for most of the response for input changes up to ±20%. ISORROPIA shows greater deviation from linear response than the other PM modules and the near-linear range can be as restricted as ±10% changes in concentration inputs. Nonlinearity in ISORROPIA results both from the equations that describe thermodynamic equilibrium and the computational approaches within ISORROPIA that are employed to boost efficiency. [ABSTRACT FROM AUTHOR]