This study presents a comparative evaluation of the impact of WRF-NMM and WRF-ARW meteorology on CMAQ simulations of PM2.5, its composition and related precursors over the eastern United States with the intensive observations obtained by aircraft (NOAA WP-3), ship and surface monitoring networks (AIRNow, IMPROVE, CASTNet and STN) during the 2006 TexAQS/GoMACCS study. The results at the AIRNow surface sites show that both ARW-CMAQ and NMM-CMAQ reproduced day-to-day variations of observed PM2.5 and captured the majority of observed PM2.5 within a factor of 2 with a NMB value of −0.4% for ARW-CMAQ and −18% for NMM-CMAQ. Both models performed much better at the urban sites than at the rural sites, with greater underpredictions at the rural sites. Both models consistently underestimated the observed PM2.5 at the rural IMPROVE sites by −1% for the ARW-CMAQ and −19% for the NMM-CMAQ. The greater underestimations of SO42−, OC and EC by the NMM-CMAQ contributed to increased underestimation of PM2.5 at the IMPROVE sites. The NMB values for PM2.5 at the STN urban sites are 15% and −16% for the ARW-CMAQ and NMM-CMAQ, respectively. The underestimation of PM2.5 at the STN sites by the NMM-CMAQ mainly results from the underestimations of the SO42−, NH4+ and TCM components, whereas the overestimation of PM2.5 at the STN sites by the ARW-CMAQ results from the overestimations of SO42−, NO3−, and NH4+. The Comparison with WP-3 aircraft measurements reveals that both ARW-CMAQ and NMM-CMAQ have very similar model performance for vertical profiles for PM2.5 chemical components (SO42−, NH4+) and related gaseous species (HNO3, SO2, NH3, isoprene, toluene, terpenes) as both models used the same chemical mechanisms and emissions. The results of ship along the coast of southeastern Texas over the Gulf of Mexico show that both models captured the temporal variations and broad synoptic change seen in the observed HCHO and acetaldehyde with the means NMB <30% most of the time but they consistently underestimated terpenes, isoprene, toluene and SO2.

Abstract. This study presents a comparative evaluation of the impact of WRF-NMM and WRF-ARW meteorology on CMAQ simulations of PM 2.5 , its composition and related precursors over the eastern United States with the intensive observations obtained by aircraft (NOAA WP-3), ship and surface monitoring networks (AIRNow, IMPROVE, CASTNet and STN) during the 2006 TexAQS/GoMACCS study. The results at the AIRNow surface sites show that both ARW-CMAQ and NMM-CMAQ reproduced day-to-day variations of observed PM 2.5 and captured the majority of observed PM 2.5 within a factor of 2 with a NMB value of −0.4% for ARW-CMAQ and −18% for NMM-CMAQ. Both models performed much better at the urban sites than at the rural sites, with greater underpredictions at the rural sites. Both models consistently underestimated the observed PM 2.5 at the rural IMPROVE sites by −1% for the ARW-CMAQ and −19% for the NMM-CMAQ. The greater underestimations of SO 4 2− , OC and EC by the NMM-CMAQ contributed to increased underestimation of PM 2.5 at the IMPROVE sites. The NMB values for PM 2.5 at the STN urban sites are 15% and −16% for the ARW-CMAQ and NMM-CMAQ, respectively. The underestimation of PM 2.5 at the STN sites by the NMM-CMAQ mainly results from the underestimations of the SO 4 2− , NH 4 + and TCM components, whereas the overestimation of PM 2.5 at the STN sites by the ARW-CMAQ results from the overestimations of SO 4 2− , NO 3 − , and NH 4 + . The Comparison with WP-3 aircraft measurements reveals that both ARW-CMAQ and NMM-CMAQ have very similar model performance for vertical profiles for PM 2.5 chemical components (SO 4 2− , NH 4 + ) and related gaseous species (HNO 3 , SO 2 , NH 3 , isoprene, toluene, terpenes) as both models used the same chemical mechanisms and emissions. The results of ship along the coast of southeastern Texas over the Gulf of Mexico show that both models captured the temporal variations and broad synoptic change seen in the observed HCHO and acetaldehyde with the means NMB 2 .

Introduction Conclusions References

Abstract. A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base) and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone concentrations. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. A sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While it increases in-cloud secondary organic aerosol substantially, its impact on total fine particle mass concentration is small.

The complexity of air quality modeling systems, air quality monitoring data make ad-hoc systems for model evaluation important aids to the modeling community. Among those are the ENSEMBLE system developed by the EC-Joint Research Center, and the AMET software developed by the US-EPA. These independent systems provide two examples of state of the art tools to support model evaluation. The two systems are described here mostly from the point of view of the support to air quality model users or developers rather than the technological point of view. While ENSEMBLE is a web based platform for model evaluation that allows the collection, share and treatment of model results as well as monitoring data, AMET is a standalone tool that works directly on single model data. The complementarity of the two approaches makes the two systems optimal for operational, diagnostic and probabilistic evaluations. ENSEMBLE and AMET have been extended in occasion of the AQMEII two-continent exercise and the new developments are described in this paper, together with those foreseen for the future.

This document is the culminating product of a series of ad-hoc workshops led by a group of European and North American air quality scientists interested in instigating a significant advance in the way regional-scale air quality modelling systems are evaluated. The initiative arising out of these workshops has come to be known as the Air Quality Model Evaluation International Initiative (AQMEII). The motivation for the workshops was a growing sense that current model evaluation practices have stagnated, and as a result are not serving research, operational and policy sectors as well as they should. While technical (often statistical) approaches have advanced in the past two decades, it was felt that present approaches are too often uncritically applied, without due consideration of the foundations upon which the techniques are based. This document contains a set of research recommendations for advancing model evaluation. These recommendations are based on ideas generated during workshop presentations, group discussions and informal discussions that occurred at and around the workshops. How to obtain EU publications Our priced publications are available from EU Bookshop (http://bookshop.europa.eu), where you can place an order with the sales agent of your choice. The Publications Office has a worldwide network of sales agents. You can obtain their contact details by sending a fax to (352) 29 29-42758. The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national. LB -N A -2445-EN -C

The main use of air quality forecast (AQF) models is to predict ozone (O3) exceedances of the primary O3 standard for informing the public of potential health concerns. This study presents the first evaluation of the performance of the Eta-CMAQ air quality forecast model to predict a variety of widely used seasonal mean and cumulative O3 exposure indices associated with vegetation using the U.S. AIRNow O3 observations. These exposure indices include two concentration-based O3 indices, M7 and M12 (the seasonal means of daytime 7-h and 12-h O3 concentrations, respectively), and three cumulative exposure-based indices, SUM06 (the sum of all hourly O3 concentrations≥0.06ppm), W126 (hourly concentrations weighed by a sigmoidal weighting function), and AOT40 (O3 concentrations accumulated over a threshold of 40ppb during daylight hours). During a three-month simulation (July–September 2005), the model over predicted the M7 and M12 values by 8–9ppb, or a NMB value of 19% and a NME value of 21%. The model predicts a central belt of high O3 extending from Southern California to Middle Atlantic where the seasonal means, M7 and M12 (the seasonal means of daytime 7-h and 12-h O3 concentrations), are higher than 50ppbv. In contrast, the model is less capable of reproducing the observed cumulative indices. For AOT40, SUM06 and W126, the NMB and NME values are two- to three-fold of that for M7, M12 or peak 8-h O3 concentrations. The AOT40 values range from 2 to 33ppmh by the model and from 1 to 40ppmh by the monitors. There is a significantly higher AOT40 value experienced in the United States in comparison to Europe. The domain-wide mean SUM06 value is 14.4ppmh, which is about 30% higher than W126, and 40% higher than AOT40 calculated from the same 3-month hourly O3 data. This suggests that SUM06 and W126 represent a more stringent standard than AOT40 if either the SUM06 or the W126 was used as a secondary O3 standard. Although CMAQ considerably over predicts SUM06 and W126 values at the low end, the model under predicts the extreme high exposure values (>50ppmh). Most of these extreme high values are found at inland California sites. Based on our analysis, further improvement of the model is needed to better capture cumulative exposure indices.

The 2006 release of the Community Multiscale Air Quality (CMAQ) model (Version 4.6) includes upgrades to several model components as well as new modules for gas-phase chemistry and boundary layer mixing. Capabilities for simulation of hazardous air pollutants have been expanded to include more gas-phase toxics and several toxic metals. The mercury model has been updated and adapted to the new version of CMAQ. The CMAQ analysis tools, sulfur tracking and carbon source apportionment, have also been updated to operate with new physics, chemistry, and aerosol components. An overview of the new upgrades for CMAQ version 4.6 is provided in Section 2. Then, in Section 3, we take a preliminary look at the model evaluation for the CMAQv4.6 release. Section 4 outlines plans for further model development over the next year and beyond. Finally, a summary is presented in Section 5.

The inorganic species of sulfate, nitrate and ammonium constitute a major fraction of atmospheric aerosols. The behavior of nitrate is one of the most intriguing aspects of inorganic atmospheric aerosols because particulate nitrate concentrations depend not only on the amount of gas-phase nitric acid, but also on the availability of ammonia and sulfate, together with temperature and relative humidity. Particulate nitrate is produced mainly from the equilibrium reaction between two gas-phase species, HNO3 and NH3.

This study investigates the potential utility of the application of a photochemical modeling system in providing simultaneous forecasts of ozone (O-3) 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 O-3 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.

The partitioning of total nitrate (TNO3) and total ammonium (TNH4) between gas and aerosol phases is studied with two thermodynamic equilibrium models, ISORROPIA and the aerosol inorganics model (AIM), and three data sets: high time resolution measurement data from the 1999 Atlanta Supersite Experiment (summer case) and the 2002 Pittsburgh Air Quality Study (PAQS) Supersite Experiment (winter case), and 12‐hour measurement data from the Clinton site, North Carolina, in 1999. At the Atlanta site, both models reproduced a large percentage of the observed aerosol NH4+ and HNO3 (NH4+: >94% and HNO3: >86%) within a factor of 1.5, whereas neither model reproduced a majority of observed aerosol NO3− and NH3 (NO3−: <48% and NH3: <51%) within a factor of 2. At the Pittsburgh site, both models reproduced more than 76% of observed NO3− within a factor of 2. At the Clinton site, both models performed a little better on aerosol NO3− (47–58% within a factor of 1.5) than at the Atlanta site but worse than at the Pittsburgh site. Sensitivity test of thermodynamic models with Gaussian random errors indicates that in many cases, measurement errors in SO42− and TNH4 can explain a major fraction of the discrepancies between the equilibrium model predictions and observations in partitioning of TNO3. Comparison of predictions of the three‐dimensional (3‐D) Community Multiscale Air Quality (CMAQ) model with the observations over the continental United States indicates that the performance of the 3‐D model for NO3−, HNO3, NH4+, and NH3 strongly depends on its performance for TNO3, TNH4, and SO42−. Tests show that errors associated with SO42− and TNH4 predictions of the 3‐D model can result in the thermodynamic model calculation replicating only 47% and 60% of base case NO3− within a factor of 2 for summer and winter cases, respectively. It was found that errors in TNH4 are more critical than errors in SO42− to prediction of NO3−.