A Preliminary Evaluation of Models-3 CMAQ Using Visibility Parameters

摘要

Ambient air concentrations of fine particulate matter (PM_(2.5)) continue to be a major concern for the U.S. Environmental Protection Agency. High concentrations of fine particles have been linked to detrimental health effects (including an increase in mortality) and visibility degradation. Accordingly, the Clean Air Act and Amendments of 1990 called for an assessment of current and future regulations designed to protect human health and welfare. The most reliable tools for carrying out such assessments are air quality models like EPA’s Community Multiscale Air Quality (CMAQ), which simulates air concentrations and deposition of PM_(2.5) (along with other pollutants) and various measures of visibility associated with specified levels of emissions. These simulations can be used by EPA Program Offices and research laboratories to support both regulatory assessment and scientific studies on a myriad of spatial and temporal scales. This paper provides a preliminary evaluation of CMAQ using a visibility parameter called the deciview. The evaluation compares deciview values computed from visibility observations at 174 stations in the eastern half of the U.S. With those simulated by the model for the 5-day period 11-15 July 1995. Visibility was selected for this evaluation for two reasons: it can serve as a surrogate for PM_(2.5), for which little observational data currently exist; and it has one of the most spatially and temporally comprehensive observational data sets available. The evaluation revealed a reasonable level of representation as CMAQ captured the basic spatial and temporal patterns of visibility degradation including major gradients and maxima/minima. The correlation coefficient between the observed and simulated deciviews for the entire simulation period was 0.56 and ranged from 0.38 on 11 July to 0.70 on 13 July. The model generally under predicted the visibility degradation by 10 deciviews; however, much of this discrepancy can be attributed to artifacts associated with the observed data.