Winston Hao

Evaluating the performance of regional-scale photochemical modeling systems: Part I—meteorological predictions

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperature, water vapor, and wind speed were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the method of spectral decomposition can help provide additional insight into the models’ performance. The results indicate that both meteorological models under-represent the variance of fluctuations on the intra-day time scale. Correlations between model predictions and observations for temperature and wind speed are insignificant on the intra-day time scale, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and longer-term components. This better model performance on longer time scales suggests that current regional-scale models are most skillful for characterizing average patterns over extended periods. The implications of these results to using meteorological models to drive photochemical models are discussed. r 2001 Elsevier Science Ltd. All rights reserved.

Effects of uncertainties in meteorological inputs on urban airshed model predictions and ozone control strategies

Abstract Although well-recognized within the photochemical modeling community, the effect of uncertainties in meteorological input on the urban airshed model (UAM) output has not been systematically evaluated. In this study, the UAM has been applied to investigate the sensitivity of ozone predictions to the choices in wind fields and mixing height profiles for the data-sparse New York metropolitan area. A set of three wind fields, in combination with spatially varying and spatially invariant mixing heights, is investigated for the July 1988 ozone episode. In general, model-predicted ozone levels were higher under the spatially varying mixing height (SVM) option than under the spatially invariant mixing height (SIM) option. SVM based UAM simulations provided better agreement between the predicted and measured ozone concentrations than SIM-based UAM simulations. However, from the regulatory standpoint, predicted ozone concentrations based on either of these mixing height options are within the range considered as acceptable. UAM simulations with emission reductions of 75% NOx and 25% VOCs (NOx-focused) reveal that the improvement in peak ozone levels under the SIM option is larger than that under the SVM option, whereas the emission reduction scenario of 25% NOx and 75% VOCs (VOC-focused) yields greater improvement in peak ozone under the SVM option than with the SIM option. Given the strong influence of mixing heights and wind fields on UAM model predictions in data-sparse areas, it is imperative that uncertainties in development of ozone abatement plans be quantified.

An Operational Evaluation of Two Regional-Scale Ozone Air Quality Modeling Systems over the Eastern United States

In this paper, the performance of two commonly used regional-scale Eulerian photochemical modeling systems, namely, RAMS/UAM-V and MM5/SAQM, from the regulatory or operational perspective, is examined. While the Urban Airshed Model with Variable Grid (UAM-V) is driven with the meteorological fields derived from the Regional Atmospheric Model System (RAMS), the San Joaquin Valley Air Quality Model (SAQM) used the meteorological fields derived from the Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model Version 5 (MM5). The models performance in reproducing the observed ozone air quality over the eastern United States is evaluated for three typical high-ozone episodic events that occurred during 16–20 June, 12–16 July, and 30 July–2 August of 1995. The prevailing meteorological conditions associated with these three episodes are characterized by a slow eastward-moving high pressure system, westerly and southwesterly low-level jets, stable boundary layers, and the Appalach...

Assessing the effects of transboundary ozone pollution between Ontario, Canada and New York, USA

We investigated the effects of transboundary pollution between Ontario and New York using both observations and modeling results. Analysis of the spatial scales associated with ozone pollution revealed the regional and international character of this pollutant. A back-trajectory-clustering methodology was used to evaluate the potential for transboundary pollution trading and to identify potential pollution source regions for two sites: CN tower in Toronto and the World Trade Center in New York City. Transboundary pollution transport was evident at both locations. The major pollution source areas for the period examined were the Ohio River Valley and Midwest. Finally, we examined the transboundary impact of emission reductions through photochemical models. We found that emissions from both New York and Ontario were transported across the border and that reductions in predicted O3 levels can be substantial when emissions on both sides of the border are reduced.

Impact of Biogenic Emission Uncertainties on the Simulated Response of Ozone and Fine Particulate Matter to Anthropogenic Emission Reductions

ABSTRACT The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1–0.25 μg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1–2% of the value of the annual PM2.5 NAAQS of 15 μg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions. IMPLICATIONS The findings presented in this study demonstrate that uncertainties in biogenic emission estimates due to different emission models can have a significant effect on the model estimates of ozone and PM2.5 concentrations; specifically, the changes in these concentrations due to reductions in anthropogenic emissions considered in regulatory modeling scenarios. These results point to the need for further research aimed at improving biogenic emission estimates as well as better characterizing their dependency on environmental factors and the fate of these emissions once released into the atmosphere.

Daily Simulation of Ozone and Fine Particulates over New York State: Findings and Challenges

Abstract 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 th...

Rethinking the Assessment of Photochemical Modeling Systems in Air Quality Planning Applications

Abstract The U.S. Environmental Protection Agency provides guidelines for demonstrating that future 8-hr ozone (O3) design values will be at or below the National Ambient Air Quality Standards on the basis of the application of photochemical modeling systems to simulate the effect of emission reductions. These guidelines also require assessment of the model simulation against observations. In this study, we examined the link between the simulated relative responses to emission reductions and model performance as measured by operational evaluation metrics, a part of the model evaluation required by the guidance, which often is the cornerstone of model evaluation in many practical applications. To this end, summertime O3 concentrations were simulated with two modeling systems for both 2002 and 2009 emission conditions. One of these two modeling systems was applied with two different parameterizations for vertical mixing. Comparison of the simulated base-case 8-hr daily maximum O3 concentrations showed marked model-to-model differences of up to 20 ppb, resulting in significant differences in operational model performance measures. In contrast, only relatively minor differences were detected in the relative response of O3 concentrations to emission reductions, resulting in differences of a few ppb or less in estimated future year design values. These findings imply that operational model evaluation metrics provide little insight into the reliability of the actual model application in the regulatory setting (i.e., the estimation of relative changes). In agreement with the guidance, it is argued that more emphasis should be placed on the diagnostic evaluation of O3-precursor relationships and on the development and application of dynamic and retrospective evaluation approaches in which the response of the model to changes in meteorology and emissions is compared with observed changes. As an example, simulated relative O3 changes between 1995 and 2007 are compared against observed changes. It is suggested that such retrospective studies can serve as the starting point for targeted diagnostic studies in which individual aspects of the modeling system are evaluated and refined to improve the characterization of observed changes.

A Retrospective Comparison of Model-Based Forecasted PM2.5 Concentrations with Measurements

Abstract This study presents an assessment of the performance of the Community Multiscale Air Quality (CMAQ) photochemical model in forecasting daily PM2.5 (particulate matter ≤2.5 µm in aerodynamic diameter) mass concentrations over most of the eastern United States for a 2-yr period from June 14, 2006 to June 13, 2008. Model predictions were compared with filter-based and continuous measurements of PM2.5 mass and species on a seasonal and regional basis. Results indicate an underprediction of PM2.5 mass in spring and summer, resulting from under-predictions in sulfate and total carbon concentrations. During winter, the model overpredicted mass concentrations, mostly at the urban sites in the northeastern United States because of overpredictions in unspeciated PM2.5 (suggesting possible overestimation of primary emissions) and sulfate. A comparison of observed and predicted diurnal profiles of PM2.5 mass at five sites in the domain showed significant discrepancies. Sulfate diurnal profiles agreed in shape across three sites in the southern portion of the domain but differed at two sites in the northern portion of the domain. Predicted organic carbon (OC) profiles were similar in shape to mass, suggesting that discrepancies in mass profiles probably resulted from the underprediction in OC. The diurnal profiles at a highly urbanized site in New York City suggested that the over-predictions at that site might be resulting from overpredictions during the morning and evening hours, displayed as sharp peaks in predicted profiles. An examination of the predicted planetary boundary layer (PBL) heights also showed possible issues in the modeling of PBL.

An Assessment of the Emissions Inventory Processing Systems EMS-2001 and SMOKE in Grid-Based Air Quality Models

Abstract In the United States, emission processing models such as Emissions Modeling System-2001 (EMS-2001), Emissions Preprocessor System-Version 2.5 (EPS2.5), and the Sparse Matrix Operator Kernel Emissions (SMOKE) model are currently being used to generate gridded, hourly, speciated emission inputs for urban and regional-scale photochemical models from aggregated pollutant inventories. In this study, two models, EMS-2001 and SMOKE, were applied with their default internal data sets to process a common inventory database for a high ozone (O3) episode over the eastern United States using the Carbon Bond IV (CB4) chemical speciation mechanism. A comparison of the emissions processed by these systems shows differences in all three of the major processing steps performed by the two models (i.e., in temporal allocation, spatial allocation, and chemical speciation). Results from a simulation with a photochemical model using these two sets of emissions indicate differences on the order of ±20 ppb in the predicted 1-hr daily maximum O3 concentrations. It is therefore critical to develop and implement more common and synchronized temporal, spatial, and speciation cross-reference systems such that the processes within each emissions model converge toward reasonably similar results. This would also help to increase confidence in the validity of photochemical grid model results by reducing one aspect of modeling uncertainty.

An Operational Assessment of the Application of the Relative Reduction Factors in the Demonstration of Attainment of the 8-Hr Ozone National Ambient Air Quality Standard

Abstract The U.S. Environmental Protection Agency in 1997 revised the 1-hr ozone (O3) National Ambient Air Quality Standard (NAAQS) to one based on an 8-hr average, resulting in potential nonattainment status for substantial portions of the eastern United States. The regulatory process provides for the development of a state implementation plan that includes a demonstration that the projected future O3 concentrations will be at or below the NAAQS based on photochemical modeling and analytical techniques. In this study, four photochemical modeling systems, based on two photochemical models, Community Model for Air Quality and the Comprehensive Air Quality Model with extensions, and two emissions processing models, Sparse Matrix Optimization Kernel for Emissions and Emissions Modeling System, were applied to the eastern United States, with emphasis on the northeastern Ozone Transport Region in terms of their response to oxides of nitrogen and volatile organic carbon-focused controls on the estimated design values. With the 8-hr O3 NAAQS set as a bright-line test, it was found that a given area could be termed as being in or out of attainment of the NAAQS depending upon the modeling system. This suggests the need to provide an estimate of model-to-model uncertainty in the relative reduction factor (RRF) for a better understanding of the uncertainty in projecting the status of an areas attainment. Results indicate that the model-to-model differences considered in this study introduce an uncertainty of the future estimated design value of ∼3–5 ppb.