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Regional air pollution episodes occur as a result of increased emissions and prevalence of conducive meteorological conditions. The frequency of occurrence of such favorable conditions on a regional scale may be influenced by large-scale climatic events like ENSO and the Pacific decadal oscillation (PDO). The scarcity of measurements of criteria pollutants, especially ozone and particulate matter (PM), prior to the last 10-15-yr period, limits the scope of observing the influence of climate variability during recent decades on regional pollution levels. The authors propose a novel statistical framework to utilize available measurements and characterize synoptic influences on regional PM pollution in California's Central Valley during 1998-2008. The identified target conditions are used to develop a classification scheme to scan historical climate datasets dating back to 1948. The procedure identifies exceedance-conducive days during 1950-98, when no PM2.5 measurements were available. Temporal patterns in seasonal frequency of these identified exceedance-conducive days are investigated for temporal patterns driven by ENSO and PDO. [ABSTRACT FROM AUTHOR]

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]

Abstract: Methods are presented to extract intra-seasonal meteorological patterns at three scales to explain 24-h fine particulate matter (PM2.5) pollution events: evolving large-scale meteorological scenarios, synoptic regimes driving diurnal variability near the surface, and localized meteorological triggers. The methods were applied to understand how winter weather conditions impacted PM2.5 around the San Francisco Bay Area (SFBA). Analyzing data across 12 winters (November–March) ensured robust characterization of the SFBA conditions. SFBA 24-h PM2.5 exceedances (35μg m−3) required several simultaneous characteristics: a ridge of aloft high pressure moving over SFBA, providing weak surface pressure gradients over Central California; persistent easterly flows through SFBA extending vertically to around the 925-hPa pressure level; orographically channeled winds resulting from stability; enhanced nocturnal cooling under clear-sky conditions providing for enhanced drainage flows off the Central California slopes; and at least two consecutive days of these conditions. [Copyright &y& Elsevier]

Prediction of air pollutant levels plays an important role in the regulatory plans aimed at the control and reduction of airborne pollutants such as fine particulate matter (PM). Deterministic photochemical air quality models, which are commonly used for regulatory management and planning, are computationally intensive and also expensive for routine predictions. Compared to deterministic photochemical air quality models, data-driven statistical models are simpler and may be more accurate. In this paper, hidden Markov models (HMM) are used to forecast daily average PM(2.5) concentrations 24 h ahead. In conventional HMM applications, observation distributions emitted from certain hidden states are assumed as having Gaussian distributions. However, certain key meteorological factors and most PM(2.5) precursors exhibit a non-Gaussian distribution in reality, which would degrade the HMM performance significantly. In order to address this problem, in this paper, HMMs with log-normal, Gamma and generalized extreme value (GEV) distributions are developed to predict PM(2.5) concentration at Concord and Sacramento monitors in Northern California. Results show that HMM with non-Gaussian emission distributions is able to predict PM(2.5) exceedance days correctly and reduces false alarms dramatically. Compared to HMM with Gaussian distributions, HMM with log-normal distributions can improve the true prediction rate (TPR) by 37.5% and reduce the false alarms by 78% at Concord. And HMM with GEV distribution can improve TPR by 150% and reduce false alarms by 63.62% at Sacramento Del Paso Manor. Comparisons between different distributions used in HMM show that the closer the distribution employed in HMM is to the observation sequence, the better the model prediction performance., (Copyright © 2012 Elsevier B.V. All rights reserved.)