Gopal Sistla

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...

The effects of land use in meteorological modeling: implications for assessment of future air quality scenarios

In recent years, there has been an increased use of prognostic meteorological models to assess current and future air quality related problems. Often, these meteorological models are applied in their forecasting mode with current land use/land cover patterns and data assimilation techniques to generate historical meteorological data for use in air quality models. In this study, we examined the sensitivity of land use/land cover on the predicted meteorological fields, and the implications for examining air quality in a future year. A community-based mesoscale meteorological model (MM5-Version 1) was applied to the northeastern US urban corridor under two scenarios, one with the existing land use/land cover (base case), and the second reflecting a hypothetical change in about 40% of the base case urban grid cells to deciduous forest. A comparison of the two meteorological fields reveals substantial localized differences in surface temperature and zonal wind speeds. These findings suggest reevaluation of the practice of using historical meteorological fields to assess future air quality, especially if one expects large changes in land use patterns.

Evaluation of the performance of RAM with the Regional Air Pollution Study data base

Abstract The RAM air quality simulation models performance is examined using the Regional Air Pollution Study (RAPS) Level-7 data base. Time series analyses were performed to test the adequacy of RAM in simulating the dynamics of pollutants in the atmosphere. Power spectrum and auto-correlation analyses show that the predicted concentrations do not have the same temporal characteristics as the observations during the winter period. Both paired and unpaired analyses are included to critically examine the model performance. The paired comparisons, including those performance measures suggested by the AMS Woods Hole workshop, indicate a better agreement between predicted and observed data at the rural sites than at the urban sites. When the data are segregated according to wind speed and atmospheric stability class, it is found that there is very little agreement between the predicted and measured concentrations under extreme stability (either very unstable or very stable) and low wind speed (less than 2 ms−1 conditions. The measured and predicted daily maximum concentrations are subjected to the ‘bootstrap’ sampling procedure to develop the distribution of the differences between observed and predicted concentrations. These distributions suggest that the errors are random, and, therefore, RAM cannot be calibrated to improve model performance within the urban area. Further, the analyses suggest that improvement of RAMs performance may be realized through a better characterization of area source emissions within the urban area and the inclusion of other physical processes such as a fumigation algorithm in the model.

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...

Statistical Analysis of Trends in Urban Ozone Air Quality

The purpose of this paper is to demonstrate the use of some statistical methods for examining trends in ambient ozone air quality downwind of major urban areas. To this end, daily maximum 1-hr ozone concentrations measured over New Jersey, metropolitan New York City and Connecticut for the period 1980 to 1989 were assembled and analyzed. This paper discusses the application of the bootstrap method, extreme value statistics and a nonparametric test for evaluating trends in urban ozone air quality. The results indicate that although there is an improvement in ozone air quality downwind of New York City, there has been little change in ozone levels upwind of New York City during this ten-year period.

A study of pollutant dispersion near highways

Abstract As a part of a major roadway dispersion project undertaken by the New York State Department of Environmental Conservation, several tracer release experiments were conducted on an open, at-grade highway site. The observed concentration profiles indicate that the tracer concentration decreases with increasing distance from the roadway more rapidly for parallel wind-road orientation than for perpendicular wind-road orientation. However, the decrease of concentration with height on the tower at the nearest, downwind roadside receptor is slow for parallel case as compared to perpendicular case. Four Gaussian dispersion models (HIWAY, GM, CALINE-2, AIRPOL-4) and four numerical models (DANARD, MROAD 2, RAGLAND, ROADS) are used to predict the tracer gas concentrations. Of the models tested, GM and HIWAY perform well compared to other Gaussian models. The numerical models performed about the same as the above two Gaussian models. The GM model provides a better simulation by far for parallel wind cases than any of the other models tested. The computed wind flow patterns indicate upward motions over the roadway in parallel wind cases which may be due to the mechanical turbulence generated by the traffic flow. The dispersion parameter values computed from the concentration measurements agree very closely to those prescribed for neutral conditions in the GM model and unstable conditions in the HIWAY model. It is suggested that the stability adjacent to the roadway may be determined either through bulk Richardson number, or wind fluctuation data obtained at the site. Further, a better formulation of the dispersion parameters may be σ z ~ σ σ x , and σ y ~ σ θ x , rather than the power law relationship ( σ z ~ α 1 x b 1 and σ y ~ α 2 x b 2 ) commonly used in the existing highway dispersion models. In this regard, wind fluctuation statistics sampled for a duration of one hour are appropriate for describing the dispersion mechanism near roadways.

An Evaluation of Some Commonly Used Highway Dispersion Models

This paper presents an evaluation of four gaussian (GM, HIWAY, AIRPOL-4, CALINE-2), and three numerical (DANARD, MROAD 2, ROADS) models with the tracer gas data collected in the General Motors experiment. Various statistical techniques are employed to quantify the predictive capability of each of the above models. In general, the three numerical models performed rather poorly compared to the gaussian models. For this data set, the model with the best performance in accurately predicting the measured concentrations was the GM model followed in order by AIRPOL-4, HIWAY, CALINE-2, DANARD, MR0AD2, and ROADS. Although the GM model provides by far a better simulation than any of the models tested here, it is skewed toward underprediction. As a screening tool for regulatory purposes, however, HIWAY model would be useful since this model has the highest percentage in the category of overprediction if the concentration data in the range of 50th percentile through 100th percentile are included in the analysis. The ...

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.