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Abstract: Regional ozone and particulate matter (PM) pollution have a strong dependence on prevailing meteorological conditions. The occurrence of favorable conditions on a regional scale may be influenced by concurrent large scale climatic events like El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). These events evolve over inter-annual and decadal timescales and may result in variability of seasonal/annual frequency of air pollution episodes. The scarcity of measurements of criteria pollutants, especially ozone and PM, limits the scope of observing the influence of climate variability in the past few decades on regional pollution levels. To identify these trends, the frequency of occurrence of conducive conditions during the summer (ozone) and winter (PM2.5) seasons are first obtained for the period 1950–2008. The identified days are subsequently utilized in a generalized linear modeling (GLM) framework to identify the effects of ENSO and PDO on the occurrence of exceedence conducive days. To demonstrate the applicability of the developed methodology, we study the influence of ENSO and PDO on summer ozone and winter PM2.5 pollution at 6 non-attainment regions in the United States. [Copyright &y& Elsevier]

A novel pattern-based model evaluation technique is proposed and demonstrated for air quality models (AQMs) driven by meteorological model (MM) output. The evaluation technique is applied directly to the MM output; however, it is ultimately used to gauge the performance of the driven AQM. This evaluation of AQM performance based on MM performance is a major advance over traditional evaluation methods. First, meteorological cluster analysis is used to assign the days of a historical measurement period among a small number of weather patterns having distinct air quality characteristics. The clustering algorithm groups days sharing similar empirical orthogonal function (EOF) representations of their measurements. In this study, EOF analysis is used to extract space-time patterns in the surface wind field reflecting both synoptic and mesoscale influences. Second, simulated wind fields are classified among the determined weather patterns using the measurement-derived EOFs. For a given period, the level of agreement between the observation-based clustering labels and the simulation-based classification labels is used to assess the validity of the simulation results. Mismatches occurring between the two sets of labels for a given period imply inaccurately simulated conditions. Moreover, the specific nature of a mismatch can help to diagnose the downstream effects of improperly simulated meteorological fields on AQM performance. This pattern-based model evaluation technique was applied to extended simulations of fine particulate matter (PM2.5) covering two winter seasons for the San Francisco Bay Area of California. [ABSTRACT FROM AUTHOR]