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.     

A retrospective comparison of model-based forecasted PM2.5 concentrations with measurements

This study presents an assessment of the performance of the Community Multiscale Air Quality (CMAQ) photochemical model in forecasting daily PM2.5 (particulate matter < or = 2.5 microm 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 overpredictions 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.     

A combined model–observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations

This paper introduces a methodology for estimating gridded fields of total and speciated fine particulate matter (PM_2.5) concentrations for time periods and regions not covered by observational data. The methodology is based on performing long-term regional scale meteorological and air quality simulations and then integrating these simulations with available observational data. To illustrate this methodology, we present an application in which year-round simulations with a meteorological model (the National Center for Atmospheric Research/Penn State Mesoscale Model, hereafter referred to as MM5) and a photochemical air quality model (the Community Multiscale Air Quality Model, hereafter referred to as CMAQ) have been performed over the northeastern United States for 1988–2005. Model evaluation results for total PM_2.5 mass and individual species for the time period from 2000 to 2005 show that model performance varies by species, season, and location. Therefore, an approach is developed to adjust CMAQ output with factors based on these three variables. The adjusted model values for total PM_2.5 mass for 2000–2005 are compared against independent measurements not utilized for the adjustment approach. This comparison reveals that the adjusted model values have a lower root mean square error (RMSE) and higher correlation coefficients than the original model values. Furthermore, the PM_2.5 estimates from these adjusted model values are compared against an alternate method for estimating historic PM_2.5 values that is based on PM_2.5/PM₁₀ ratios calculated at co-located monitors. Results reveal that both methods yield estimates of historic PM_2.5 mass that are broadly consistent; however, the adjusted CMAQ values provide greater spatial coverage and information for PM_2.5 species in addition to total PM_2.5 mass. Finally, strengths and limitations of the proposed approach are discussed in the context of potential uses of this method.     

Numerical Investigation of the Effects of Boundary-Layer Evolution on the Predictions of Ozone and the Efficacy of Emission Control Options in the Northeastern United States

This paper examines the effects of two different planetary boundary-layer (PBL) parameterization schemes – Blackadar and Gayno–Seaman – on the predicted ozone (O₃) concentration fields using the MM5 (Version 3.3) meteorological model and the MODELS-3 photochemical model. The meteorological fields obtained from the two boundary-layer schemes have been used to drive the photochemical model to simulate O₃ concentrations in the northeastern United States for a three-day O₃ episodic period. In addition to large differences in the predicted O₃ levels at individual grid cells, the simulated daily maximum 1-h O₃ concentrations appear at different regions of the modeling domain in these simulations, due to the differences in the vertical exchange formulations in these two PBL schemes. Using process analysis, we compared the differences between the different simulations in terms of the relative importance of chemical and physical processes to O₃ formation and destruction over the diurnal cycle. Finally, examination of the photochemical model's response to reductions in emissions reveals that the choice of equally valid boundary-layer parameterizations can significantly influence the efficacy of emission control strategies.     

Numerical Investigation of Boundary-Layer Evolution and Nocturnal Low-Level Jets: Local versus Non-Local PBL Schemes

Numerical simulations of the evolution of the planetary boundary layer (PBL) and nocturnal low-level jets (LLJ) have been carried out using MM5 (version 3.3) with four-dimensional data assimilation (FDDA) for a high pollution episode in the northeastern United States during July 15–20, 1999. In this paper, we assess the impact of different parameterizations on the PBL evolution with two schemes: the Blackadar PBL, a hybrid local (stable regime) and non-local (convective regime) mixing scheme; and the Gayno–Seaman PBL, a turbulent kinetic energy (TKE)-based eddy diffusion scheme. No FDDA was applied within the PBL to evaluate the ability of the two schemes to reproduce the PBL structure and its temporal variation. The restriction of the application of FDDA to the atmosphere above the PBL or the lowest 8 model levels, whichever is higher, has significantly improved the predicted strength and timing of the LLJ during the night. A systematic analysis of the PBL evolution has been performed for the primary meteorological fields (temperature, specific humidity, horizontal winds) and for the derived parameters such as the PBL height, virtual potential temperature, relative humidity, and cloud cover fraction. There are substantial differences between the PBL structures and evolutions simulated by these two different schemes. The model results were compared with independent observations (that were not used in FDDA) measured by aircraft, RASS and wind profiler, lidar, and tethered balloon platforms during the summer of 1999 as part of the NorthEast Oxidant and Particle Study (NE-OPS). The observations tend to support the non-local mixing mechanism better than the layer-to-layer eddy diffusion in the convective PBL.     

Impact of biogenic emission uncertainties on the simulated response of ozone and fine particulate matter to anthropogenic emission reductions

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 microg/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 microg/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.     

A diagnostic comparison of measured and model-predicted speciated VOC concentrations

This study compares speciated model-predicted concentrations (i.e., mixing ratios) of volatile organic compounds (VOCs) with measurements from the Photochemical Assessment Monitoring Stations (PAMS) network at sites within the northeastern US during June–August of 2006. Measurements of total non-methane organic compounds (NMOC), ozone (O₃), oxides of nitrogen (NO_x) and reactive nitrogen species (NO_y) are used for supporting analysis. The measured VOC species were grouped into the surrogate classes used by the Carbon Bond IV (CB4) chemical mechanism. It was found that the model typically over-predicted all the CB4 VOC species, except isoprene, which might be linked to overestimated emissions. Even with over-predictions in the CB4 VOC species, model performance for daily maximum O₃ was typically within ±15%. Analysis at an urban site in NY, where both NMOC and NO_x data were available, suggested that the reasonable ozone performance may be possibly due to compensating overestimated NO_x concentrations, thus modulating the NMOC/NO_x ratio to be in similar ranges as that of observations.     

Rethinking the assessment of photochemical modelin systems in air quality planning applications

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.     

Dynamic evaluation of a regional air quality model: Assessing the emissions-induced weekly ozone cycle

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system for the “weekend ozone effect” to determine if observed changes in ozone due to weekday-to-weekend (WDWE) reductions in precursor emissions can be accurately simulated. The weekend ozone effect offers a unique opportunity for dynamic evaluation, as it is a widely documented phenomenon that has persisted since the 1970s. In many urban areas of the Unites States, higher ozone has been observed on weekends than weekdays, despite dramatically reduced emissions of ozone precursors (nitrogen oxides [NO_x] and volatile organic compounds [VOCs]) on weekends. More recent measurements, however, suggest shifts in the spatial extent or reductions in WDWE ozone differences. Using 18 years (1988–2005) of observed and modeled ozone and temperature data across the northeastern United States, we re-examine the long-term trends in the weekend effect and confounding factors that may be complicating the interpretation of this trend and explore whether CMAQ can replicate the temporal features of the observed weekend effect. The amplitudes of the weekly ozone cycle have decreased during the 18-year period in our study domain, but the year-to-year variability in weekend minus weekday (WEWD) ozone amplitudes is quite large. Inter-annual variability in meteorology appears to influence WEWD differences in ozone, as well as WEWD differences in VOC and NO_x emissions. Because of the large inter-annual variability, modeling strategies using a single episode lasting a few days or a few episodes in a given year may not capture the WEWD signal that exists over longer time periods. The CMAQ model showed skill in predicting the absolute values of ozone concentrations during the daytime. However, early morning NO_x concentrations were underestimated and ozone levels were overestimated. Also, the modeled response of ozone to WEWD differences in emissions was somewhat less than that observed. This study reveals that model performance may be improved by (1) properly estimating mobile source NO_x emissions and their temporal distributions, especially for diesel vehicles; (2) reducing the grid cell size in the lowest layer of CMAQ; and, (3) using time-dependent and more realistic boundary conditions for the CMAQ simulations.     

Analysis of Toxic Air Contaminant Data Containing Concentrations Below the Limit of Detection

Air quality data often contain several observations reported only as below the analytical limit of detection (LOD), resulting in “censored” data sets. Such censored and/or truncated data sets tend to complicate statistical analysis. This paper discusses various procedures for estimating mean concentration and Its 95 percent confidence bounds from air contaminant data sets which contain values that are reported as below the LOD, A quantitative approach for assessing the uncertainty Inherent In the estimated mean concentration due to (a) presence of values below the LOD In the data set, and (b) natural variability of atmospheric concentration data, is described. The utility of this approach In the analysis and interpretation of ambient pollutant concentration data Is demonstrated for both hypothetical and observed singly-censored data sets, and for a multiply-censored, multi-pollutant observed concentration data set. The methodologies discussed here should be particularly useful In verifying compliance with environmental regulations, and In estimating the risks associated with long-term exposure of populations to toxic air contaminants and assessing the uncertainty associated with these estimates.