Development of a United States-Mexico Emissions Inventory for the Big Bend Regional Aerosol and Visibility Observational (BRAVO) Study

The Big Bend Regional Aerosol and Visibility Observational (BRAVO) Study was commissioned to investigate the sources of haze at Big Bend National Park in southwest Texas. The modeling domain of the BRAVO Study includes most of the continental United States and Mexico. The BRAVO emissions inventory was constructed from the 1999 National Emission Inventory for the United States, modified to include finer-resolution data for Texas and 13 U.S. states in close proximity. The first regional-scale Mexican emissions inventory designed for air-quality modeling applications was developed for 10 northern Mexican states, the Tula Industrial Park in the state of Hidalgo, and the Popocatépetl volcano in the state of Puebla. Emissions data were compiled from numerous sources, including the U.S. Environmental Protection Agency (EPA), the Texas Natural Resources Conservation Commission (now Texas Commission on Environmental Quality), the Eastern Research Group, the Minerals Management Service, the Instituto Nacional de Ecología, and the Instituto Nacional de Estadistica Geografía y Informática. The inventory includes emissions for CO, nitrogen oxides, sulfur dioxide, volatile organic compounds (VOCs), ammonia, particulate matter (PM) < 10 microm in aerodynamic diameter, and PM < 2.5 microm in aerodynamic diameter. Wind-blown dust and biomass burning were not included in the inventory, although high concentrations of dust and organic PM attributed to biomass burning have been observed at Big Bend National Park. The SMOKE modeling system was used to generate gridded emissions fields for use with the Regional Modeling System for Aerosols and Deposition (REMSAD) and the Community Multiscale Air Quality model modified with the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (CMAQ-MADRID). The compilation of the inventory, supporting model input data, and issues encountered during the development of the inventory are documented. A comparison of the BRAVO emissions inventory for Mexico with other emerging Mexican emission inventories illustrates their uncertainty.     

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.     

Evaluation of the SO2 and NOX offset ratio method to account for secondary PM2.5 formation

The U.S. Environmental Protection Agency (EPA), state and local agencies have focused their efforts in assessing secondary fine particulate matter (aerodynamic diameter ≤2.5 µm; PM_2.5) formation in prevention of significant deterioration (PSD) air dispersion modeling. The National Association of Clean Air Agencies (NACAA) developed a method to account for secondary PM_2.5 formation by using sulfur dioxide (SO₂) and nitrogen oxides (NO_x) offset ratios. These ratios are used to estimate the secondary formation of sulfate and nitrate PM_2.5. These ratios were first introduced by the EPA for nonattainment areas in the Implementation of the New Source Review (NSR) Program for Particulate Matter Less than 2.5 Micrometers (PM_2.5), 73 FR 28321, to offset emission increases of direct PM_2.5 emissions with reductions of PM_2.5 precursors and vice versa. Some regulatory agencies such as the Minnesota Pollution Control Agency (MPCA) have developed area-specific offset ratios for SO₂ and NO_x based on Comprehensive Air Quality Model with Extensions (CAMx) evaluations for air dispersion modeling analyses. The current study evaluates the effect on American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) predicted concentrations from the use of EPA and MPCA developed ratios. The study assesses the effect of these ratios on an electric generating utility (EGU), taconite mine, food processing plant, and a pulp and paper mill. The inputs used for these four scenarios are based on common stack parameters and emissions based on available data. The effect of background concentrations also evaluates these scenarios by presenting results based on uniform annual PM_2.5 background values. This evaluation study helps assess the viability of the offset ratio method developed by NACAA in estimating primary and secondary PM_2.5 concentrations. An alternative Tier 2 approach to combine modeled and monitored concentrations is also presented.

Implications:

On January 4, 2012, the EPA committed to engage in rulemaking to evaluate updates to the Guideline on Air Quality Models (Appendix W of 40 CFR 51) and, as appropriate, incorporate new analytical techniques or models for secondary PM_2.5. As a result, the National Association of Clean Air Agencies (NACAA) developed a screening method involving offset ratios to account for secondary PM_2.5 formation. The use of this method is promising to evaluate total (direct and indirect) PM_2.5 impacts for permitting purposes. Therefore, the evaluation of this method is important to determine its viability for widespread use.     

Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune,India

The prediction of spatial variation of the concentration of a pollutant governed by various sources and sinks is a complex problem. Gaussian air pollutant dispersion models such as AERMOD of the United States Environmental Protection Agency (USEPA) can be used for this purpose. AERMOD requires steady and horizontally homogeneous hourly surface and upper air meteorological observations. However, observations with such frequency are not easily available for most locations in India. To overcome this limitation, the planetary boundary layer and surface layer parameters required by AERMOD were computed using the Weather Research and Forecasting (WRF) Model (version 2.1.1) developed by the National Center for Atmospheric Research (NCAR). We have developed a preprocessor for offline coupling of WRF with AERMOD. Using this system, the dispersion of respirable particulate matter (RSPM/PM10) over Pune, India has been simulated. Data from the emissions inventory development and field-monitoring campaign (13–17 April 2005) conducted under the Pune Air Quality Management Program of the Ministry of Environment and Forests (MoEF), India and USEPA, have been used to drive and validate AERMOD. Comparison between the simulated and observed temperature and wind fields shows that WRF is capable of generating reliable meteorological inputs for AERMOD. The comparison of observed and simulated concentrations of PM10 shows that the model generally underestimates the concentrations over the city. However, data from this single case study would not be sufficient to conclude on suitability of regionally averaged meteorological parameters for driving Gaussian models like AERMOD and additional simulations with different WRF parameterizations along with an improved pollutant source data will be required for enhancing the reliability of the WRF–AERMOD modeling system.     

PM10 dispersion modeling for Treasure Valley, Idaho

The recorded exceedances of the 24-hr PM10 National Ambient Air Quality Standard (NAAQS) in Treasure Valley, Idaho, have been associated with prolonged stagnation periods during the winter. A comprehensive modeling study of PM10 impact in Treasure Valley was performed to support the State Implementation Plan (SIP). The study included base-year and short-term episodic conditions. The ISCST3 (Industrial Source Complex Short Term 3) model, using the base-year meteorology and gridded emissions of mobile sources, point sources, and wood burning as input, generally agreed well with measurements in both temporal patterns and annual averages. The WYNDvalley model was evaluated using monitoring data and was used to simulate the PM10 impact for episodic exceedances during stagnant winter conditions. An emission inventory was prepared for a base year (1995) and then extrapolated to the years 2000, 2005, 2010, and 2015 in order to determine air quality planning requirements. According to the simulations using base-year emissions and meteorology, exceedances are not expected. However, exceedances at some stations could be expected using projected emissions and episodic meteorology. Results from emission control strategies we developed indicate that mobile-source emissions have the most significant impact; reduction of 25% would be needed to eliminate the simulated exceedances in all projected years.     

Modeling traffic air pollution in street canyons in New York City for intra-urban exposure assessment in the US Multi-Ethnic Study of atherosclerosis and air pollution

We evaluated the Danish AirGIS air quality and exposure model system using air quality measurement data from New York City in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Measurements were used from three US EPA Air Quality System (AQS) monitoring stations and a comprehensive MESA Air measurement campaign including about 150 different locations and about 650 samples of about 2 week measurements of NO_x, NO₂ and PM_2.5. AirGIS is a deterministic exposure model system based on the dispersion models Operational Street Pollution Model (OSPM) and the Urban Background Model (UBM). The UBM model reproduced the annual levels within 1–26% depending on station and pollutant at the three urban background EPA monitor stations, and generally reproduced well the seasonal and diurnal variation. The full model with OSPM and UBM reproduced the MESA Air measurements with a correlation coefficient of r² = 0.51 for NO_x, r² = 0.28 for NO₂ and r² = 0.73 for PM_2.5.     

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.     

Objectives and Design of Central California's 1995 Integrated Monitoring Study of the California Regional PM10/PM2.5 Air Quality Study

ABSTRACT

The 1995 Integrated Monitoring Study (IMS95) is part of the Phase 1 planning efforts for the California Regional PM_10/PM_2.5 Air Quality Study. Thus, the overall objectives of IMS95 are to (1) fill information gaps needed for planning an effective field program later this decade; (2) develop an improved conceptual model for pollution buildup (PM10, PM2.5, and aerosol precursors) in the San Joaquin Valley; (3) develop a uniform air quality, meteorological, and emissions database that can be used to perform initial evaluations of aerosol and fog air quality models; and (4) provide early products that can be used to help with the development of State Implementation Plans for PM_10. Consideration of the new particulate matter standards were also included in the planning and design of IMS95, although they were proposed standards when IMS95 was in the planning process.     

Source apportionment of airborne particulate matter in Southeast Texas using a source-oriented 3D air quality model

A nested version of the source-oriented externally mixed UCD/CIT model was developed to study the source contributions to airborne particulate matter (PM) during a two-week long air quality episode during the Texas 2000 Air Quality Study (TexAQS 2000). Contributions to primary PM and secondary ammonium sulfate in the Houston–Galveston Bay (HGB) and Beaumont–Port Arthur (BPA) areas were determined.The predicted 24-h elemental carbon (EC), organic compounds (OC), sulfate, ammonium ion and primary PM_2.5 mass are in good agreement with filter-based observations. Predicted concentrations of hourly sulfate, ammonium ion, and primary OC from diesel and gasoline engines and biomass burning organic aerosol (BBOA) at La Porte, Texas agree well with measurements from an Aerodyne Aerosol Mass Spectrometer (AMS).The UCD/CIT model predicts that EC is mainly from diesel engines and majority of the primary OC is from internal combustion engines and industrial sources. Open burning contributes large fractions of EC, OC and primary PM_2.5 mass. Road dust, internal combustion engines and industries are the major sources of primary PM_2.5. Wildfire dominates the contributions to all primary PM components in areas near the fires. The predicted source contributions to primary PM are in general agreement with results from a chemical mass balance (CMB) model. Discrepancy between the two models suggests that further investigations on the industrial PM emissions are necessary.Secondary ammonium sulfate accounts for the majority of the secondary inorganic PM. Over 80% of the secondary sulfate in the 4 km domain is produced in upwind areas. Coal combustion is the largest source of sulfate. Ammonium ion is mainly from agriculture sources and contributions from gasoline vehicles are significant in urban areas.     

Synoptic evaluation of regional PM2.5 concentrations

By comparing short-term fluctuations in PM2.5 species concentrations among nearby air quality monitors and among species, it becomes possible to understand the regional and local events leading to higher concentrations. This approach was applied at thirteen sites in the Maryland area for the 2001–2006 timeframe in order to identify and explain the behavior of eighteen different analytes as well as the daily Air Quality Index.Findings included identification of local upwind events such as fireworks displays, construction and demolition, the spatial extent of sulfate, nitrate, and ammonium correlations between ground-level monitors, correlations between some crustal species to indicate similar emissions sources in urban areas, and indicators of particle adsorption as a rate-limiting step for certain species. For example, the bromine behavior suggests that bromine concentrations on particulate matter may be limited by the particle adsorption rate and thus show a dependence on the Air Quality Index measurements.     

Emission strength validation using four-dimensional data assimilation: application to primary aerosol and precursors to ozone and secondary aerosol.

Three-dimensional air quality models (AQMs) represent the most powerful tool to follow the dynamics of air pollutants at urban and regional scales. Current AQMs can account for the complex interactions between gas-phase chemistry, aerosol growth, cloud and scavenging processes, and transport. However, errors in model applications still exist due in part to limitations in the models themselves and in part to uncertainties in model inputs. Four-dimensional data assimilation (FDDA) can be used as a top-down tool to validate several of the model inputs, including emissions inventories, based on ambient measurements. Previously, this FDDA technique was used to estimate adjustments in the strength and composition of emissions of gas-phase primary species and O3 precursors. In this paper, we present an extension to the FDDA technique to incorporate the analysis of particulate matter (PM) and its precursors. The FDDA approach consists of an iterative optimization procedure in which an AQM is coupled to an inverse model, and adjusting the emissions minimizes the difference between ambient measurements and model-derived concentrations. Here, the FDDA technique was applied to two episodes, with the modeling domain covering the eastern United States, to derive emission adjustments of domainwide sources of NO., volatile organic compounds (VOCs), CO, SO2, NH3, and fine organic aerosol emissions. Ambient measurements used include gas-phase inorganic and organic species and speciated fine PM. Results for the base-case inventories used here indicate that emissions of SO2 and CO appear to be estimated reasonably well (requiring minor revisions), while emissions of NOx, VOC, NH3, and organic PM with aerodynamic diameter less than 2.5 microm (PM2.5) require more significant revision.     

A Comparative Validation of the RAM and PTMTP Models for Short-Term SO2 Concentrations in Two Urban Areas

A comprehensive and comparative model validation of two EPA models for short-term SO₂ concentrations was performed. The two models tested were RAM (Urban version) and PTMTP (Terrain version). Both are multiple source, multiple receptor gaussian plume models, recommended in the EPA Guideline On Air Quality Models. ¹ The principal difference between the two models is in their use of empirical dispersion coefficients. It was because of the potential for markedly different predicted maximum SO₂ concentrations, and the absence of any testing data on the RAM model, that the validation analysis was undertaken. The current study utilized a full year of air quality data from monitoring sites in two Indiana cities, Michigan City and Indianapolis. Cumulative frequency distributions for each site and model were prepared and comparisons made. The results indicate that the RAM (Urban) model was highly inaccurate in predicting maximum short-term SO₂ concentrations. The PTMTP model, although conservative in its estimates, produces results which more closely resemble the distribution of observed SO₂ concentrations. The body of information presented in this paper is directed to environmental scientists responsible for air quality modeling, and to those persons who set policy on the use of models in air quality studies.     

A modeling study of coarse particulate matter pollution in Beijing: regional source contributions and control implications for the 2008 Summer Olympics

In the last 10 yr, Beijing has made a great effort to improve its air quality. However, it is still suffering from regional coarse particulate matter (PM10) pollution that could be a challenge to the promise of clean air during the 2008 Olympics. To provide scientific guidance on regional air pollution control, the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ) air quality modeling system was used to investigate the contributions of emission sources outside the Beijing area to pollution levels in Beijing. The contributions to the PM10 concentrations in Beijing were assessed for the following sources: power plants, industry, domestic sources, transportation, agriculture, and biomass open burning. In January, it is estimated that on average 22% of the PM10 concentrations can be attributed to outside sources, of which domestic and industrial sources contributed 37 and 31%, respectively. In August, as much as 40% of the PM10 concentrations came from regional sources, of which approximately 41% came from industry and 31% from power plants. However, the synchronous analysis of the hourly concentrations, regional contributions, and wind vectors indicates that in the heaviest pollution periods the local emission sources play a more important role. The implications are that long-term control strategies should be based on regional-scale collaborations, and that emission abatement of local sources may be more effective in lowering the PM10 concentration levels on the heavy pollution days. Better air quality can be attained during the Olympics by placing effective emission controls on the local sources in Beijing and by controlling emissions from industry and power plants in the surrounding regions.     

Generalized Rollback Modeling for Urban Air Pollution Control

A general formula is derived that can be used to calculate the reductions in emissions of inert pollutants required to achieve National Ambient Air Quality Standards (NAAQS) and to predict future urban atmospheric concentrations. The derivation incorporates the main features of atmospheric diffusion modeling and takes account of all categories of sources and their spatial distribution. In our previous paper, carbon monoxide (CO) emissions from light duty vehicles were considered separately with the approximation that emissions from other sources of CO would grow and be controlled proportionately to that of light duty vehicles.

The new general formula is applied to Phoenix-Tucson using EPA data. It Is found that Phoenix-Tucson will meet the NAAQS for CO by 1985 if a 12 g/mi light duty vehicle emission standard is adopted. The EPA, using the same data in a modified rollback analysis, had predicted that Phoenix-Tucson, as well as a number of other localities, would not achieve the NAAQS even if the 3.4 g/mi statutory standard went into effect on schedule.

The underlying reasons for these very different predictions can be readily identified by means of the general formula. It is essential that the data and parameters used in these predictions be internally consistent. It is also noted that the current Federal Test Procedure (CVS-CH) for vehicle emissions gives data inconsistent with that needed to predict CO air quality with a correct methodology.     

Source apportionment of fine particles in Tennessee using a source-oriented model

Source apportionment of fine particles (PM2.5, particulate matter < 2 microm in aerodynamic diameter) is important to identify the source categories that are responsible for the concentrations observed at a particular receptor. Although receptor models have been used to do source apportionment, they do not fully take into account the chemical reactions (including photochemical reactions) involved in the formation of secondary fine particles. Secondary fine particles are formed from photochemical and other reactions involving precursor gases, such as sulfur dioxide, oxides of nitrogen, ammonia, and volatile organic compounds. This paper presents the results of modeling work aimed at developing a source apportionment of primary and secondary PM2.5. On-road mobile source and point source inventories for the state of Tennessee were estimated and compiled. The national emissions inventory for the year 1999 was used for the other states. U.S. Environmental Protection Agency Models3/Community Multi-Scale Air Quality modeling system was used for the photochemical/secondary particulate matter modeling. The modeling domain consisted of a nested 36-12-4-km domain. The 4-km domain covered the entire state of Tennessee. The episode chosen for the modeling runs was August 29 to September 9, 1999. This paper presents the approach used and the results from the modeling and attempts to quantify the contribution of major source categories, such as the on-road mobile sources (including the fugitive dust component) and coal-fired power plants, to observed PM2.5 concentrations in Tennessee. The results of this work will be helpful in policy issues targeted at designing control strategies to meet the PM2.5 National Ambient Air Quality Standards in Tennessee.     

Evaluating Sources of Indoor Air Pollution

Evaluation of Indoor air pollution problems requires an understanding of the relationship between sources, air movement, and outdoor air exchange. Research is underway to investigate these relationships. A three-phase program is being implemented: 1) Environmental chambers are used to provide source emission factors for specific indoor pollutants; 2) An IAQ (Indoor Air Quality) model has been developed to calculate indoor pollutant concentrations based on chamber emissions data and the air exchange and air movement within the indoor environment; and 3) An IAQ test house is used to conduct experiments to evaluate the model results. Examples are provided to show how this coordinated approach can be used to evaluate specific sources of indoor air pollution. Two sources are examined: 1) para-dichlorobenzene emissions from solid moth repellant; and 2) particle emissions from unvented kerosene heaters.

The evaluation process for both sources followed the three-phase approach discussed above. Para-dichlorobenzene emission factors were determined by small chamber testing at EPA’s Air and Energy Engineering Research Laboratory. Particle emission factors for the kerosene heaters were developed In large chambers at the J. B. Pierce Foundation Laboratory. Both sources were subsequently evaluated in EPA’s IAQ test house. The IAQ model predictions showed good agreement with the test house measurements when appropriate values were provided for source emissions, outside air exchange, in-house air movement, and deposition on “sink” surfaces.     

What have we learned from highly time-resolved measurements during EPA's Supersites Program and related studies?

A wide range of new and exciting highly time-resolved instruments were deployed during the U.S. Environmental Protection Agency (EPA) Supersite program and related studies that occurred during the same time period. These measurements elucidated the temporal variation of a suite of gas-phase species, particle physical properties, and size-resolved particulate chemical composition. Because the temporal resolution was so high, concentration and size distribution changes as short as 1 min or less were discerned. Often data from multiple instruments were correlated with each other and with meteorological measurements, and these correlations enabled conclusions to be drawn about the photochemical activity of the atmosphere, the location of point sources, and even the emissions characteristics of these sources. For instance, rapid changes in particulate matter (PM) concentration were due to meteorological conditions, emissions, and plume excursions that led to increases in nitrate, sulfate, and organic carbon concentrations. This paper summarizes the conclusions that have been reached, to date, using these new, highly time-resolved instruments, and demonstrates their promise for future studies.     

Comparison of regional and global land cover products and the implications for biogenic emission modeling

Accurate estimates of biogenic emissions are required for air quality models that support the development of air quality management plans and attainment demonstrations. Land cover characterization is an essential driving input for most biogenic emissions models. This work contrasted the global Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product against a regional land cover product developed for the Texas Commissions on Environmental Quality (TCEQ) over four climate regions in eastern Texas, where biogenic emissions comprise a large fraction of the total inventory of volatile organic compounds (VOCs) and land cover is highly diverse. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) was utilized to investigate the influences of land cover characterization on modeled isoprene and monoterpene emissions through changes in the standard emission potential and emission activity factor, both separately and simultaneously. In Central Texas, forest coverage was significantly lower in the MODIS land cover product relative to the TCEQ data, which resulted in substantially lower estimates of isoprene and monoterpene emissions by as much as 90%. Differences in predicted isoprene and monoterpene emissions associated with variability in land cover characterization were primarily caused by differences in the standard emission potential, which is dependent on plant functional type. Photochemical modeling was conducted to investigate the effects of differences in estimated biogenic emissions associated with land cover characterization on predicted ozone concentrations using the Comprehensive Air Quality Model with Extensions (CAMx). Mean differences in maximum daily average 8-hour (MDA8) ozone concentrations were 2 to 6 ppb with maximum differences exceeding 20 ppb. Continued focus should be on reducing uncertainties in the representation of land cover through field validation.

Implications: Uncertainties in the estimation of biogenic emissions associated with the characterization of land cover in global and regional data products were examined in eastern Texas. Misclassification between trees and low-growing vegetation in central Texas resulted in substantial differences in isoprene and monoterpene emission estimates and predicted ground-level ozone concentrations. Results from this study indicate the importance of land cover validation at regional scales.     

MNEQA,an emissions model for photochemical simulations

This study focuses on a new emissions model, Numerical Emissions Model for Air Quality (MNEQA), to be used in photochemical simulations and emission control strategies relating to tropospheric ozone pollutants. MNEQA processes available local information from external files and is easily adaptable to any desired spatial resolution. Top-down and bottom-up methodologies are combined to calculate emissions at the required resolution for photochemical simulations. It was used in conjunction with the MM5-CMAQ air quality modelling system and was applied to an episode of high ozone levels in June 2003. Emission results are widely analysed showing a difference of ?8.8% with EMEP NOx emissions, and ?18.7% with EMEP VOC emissions. Related to ozone simulations, comparative results between measurements and simulations indicated good behaviour of the model in reproducing diurnal ozone concentrations, as statistical values fall within the EPA and EU regulatory frameworks.