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This paper examines the uncertainty associated with photochemical modeling using the Variable-Grid Urban Airshed Model (UAM-V) with two different prognostic meteorological models. The meteorological fields for ozone episodes that occurred during 17-20 June, 12-15 July, and 30 July-2 August in the summer of 1995 were derived from two meteorological models, the Regional Atmospheric Modeling System (RAMS) and the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The simulated ozone concentrations from the two photochemical modeling systems, namely, RAMS/ UAM-V and MM5/UAM-V, are compared with each other and with ozone observations from several monitoring sites in the eastern United States. The overall results indicate that neither modeling system performs significantly better than the other in reproducing the observed ozone concentrations. The results reveal that there is a significant variability, about 20% at the 95% level of confidence, in the modeled 1-h ozone concentration maxima from one modeling system to the other for a given episode. The model-to-model variability in the simulated ozone levels is for most part attributable to the unsystematic type of errors. The directionality for emission controls (i.e., NO x versus VOC sensitivity) is also evaluated with UAM-V using hypothetical emission reductions. The results reveal that not only the improvement in ozone but also the VOC-sensitive and NO x-sensitive regimes are influenced by the differences in the meteorological fields. Both modeling systems indicate that a large portion of the eastern United States is NO x limited, but there are model-to-model and episode-to-episode differences at individual grid cells regarding the efficacy of emission reductions.

In this study, potential regional and local sources influencing PM2.5 (particulate matter with an aerodynamic diameter >2.5 μm) concentrations in Delhi, India, are identified and their possible impact evaluated through diverse approaches based on study of variability of synoptic and local airflow patterns that transport aerosol concentrations from these emission sources to an urban receptor site in Delhi, India. Trajectory clustering of 72-hr and 48-hr back trajectories simulated at arrival heights of 500 m and 100 m, respectively, every hour for representative years 2008-2010 are used to assess the relative influence of long-distance, regional, and subregional sources on this site. Nonparametric statistical procedures are employed on trajectory clusters to better delineate various distinct regional pollutant source regions. Trajectory clustering and concentration-weighted trajectory (CWT) analyses indicate that regional and subregional PM2.5 emission sources in neighboring country of Pakistan and adjacent states of Punjab, Haryana, and Uttar Pradesh contribute significantly to the total surplus of aerosol concentrations in the Delhi region. Conditional probability function and Bayesian approach used to identify local source regions have established substantial influence from highly urbanized satellite towns located southwest (above 25%) and southeast (above 45%) of receptor location. There is significant seasonal variability in synoptic and local air circulation patterns, which is discerned in variability in seasonal concentrations. Mean of daily averaged PM2.5 concentrations at the Income Tax Office (ITO) receptor site over Delhi at 95% confidence level is highest in winter, ranging between 209 and 185 μg m⁻³ for the entire study period. The annual variability in air transport pathways is more in winter than in other seasons. Year-to-year variability is present in aerosol concentrations, especially during winter, with standard deviations varying from a minimum of 60 µg m⁻³ in winter 2009 to a maximum of 109 µg m⁻³ in winter 2010.

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