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C. Hogrefe
W. Hao
K. Civerolo
J.-Y. Ku
G. Sistla
R. S. Gaza
L. Sedefian
K. Schere
A. Gilliland
, and
R. Mathur


This study investigates the potential utility of the application of a photochemical modeling system in providing simultaneous forecasts of ozone (O3) and fine particulate matter (PM2.5) over New York State. To this end, daily simulations from the Community Multiscale Air Quality (CMAQ) model for three extended time periods during 2004 and 2005 have been performed, and predictions were compared with observations of ozone and total and speciated PM2.5. Model performance for 8-h daily maximum O3 was found to be similar to other forecasting systems and to be better than that for the 24-h-averaged total PM2.5. Both pollutants exhibited no seasonal differences in model performance. CMAQ simulations successfully captured the urban–rural and seasonal differences evident in observed total and speciated PM2.5 concentrations. However, total PM2.5 mass was strongly overestimated in the New York City metropolitan area, and further analysis of speciated observations and model predictions showed that most of this overprediction stems from organic aerosols and crustal material. An analysis of hourly speciated data measured in Bronx County, New York, suggests that a combination of uncertainties in vertical mixing, magnitude, and temporal allocation of emissions and deposition processes are all possible contributors to this overprediction in the complex urban area. Categorical evaluation of CMAQ simulations in terms of exceeding two different threshold levels of the air quality index (AQI) again indicates better performance for ozone than PM2.5 and better performance for lower exceedance thresholds. In most regions of New York State, the routine air quality forecasts based on observed concentrations and expert judgment show slightly better agreement with the observed distributions of AQI categories than do CMAQ simulations. However, CMAQ shows skill similar to these routine forecasts in terms of capturing the AQI tendency, that is, in predicting changes in air quality conditions. Overall, the results presented in this study reveal that additional research and development is needed to improve CMAQ simulations of PM2.5 concentrations over New York State, especially for the New York City metropolitan area. On the other hand, because CMAQ simulations capture urban–rural concentration gradients and day-to-day fluctuations in observed air quality despite systematic overpredictions in some areas, it would be useful to develop tools that combine CMAQ’s predictive capability in terms of spatial concentration gradients and AQI tendencies with real-time observations of ambient pollutant levels to generate forecasts with higher temporal and spatial resolutions (e.g., county level) than those of techniques based exclusively on monitoring data.

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