An assessment of the emissions inventory processing systems EMS-2001 and SMOKE in grid-based air quality models

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.     

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Representative profiles for particulate matter particles less than or equal to 2.5 µm (PM_2.5) are developed from the Kansas City Light-Duty Vehicle Emissions Study for use in the U.S. Environmental Protection Agency (EPA) vehicle emission model, the Motor Vehicle Emission Simulator (MOVES), and for inclusion in the EPA SPECIATE database for speciation profiles. The profiles are compatible with the inputs of current photochemical air quality models, including the Community Multiscale Air Quality Aerosol Module Version 6 (AE6). The composition of light-duty gasoline PM_2.5 emissions differs significantly between cold start and hot stabilized running emissions, and between older and newer vehicles, reflecting both impacts of aging/deterioration and changes in vehicle technology. Fleet-average PM_2.5 profiles are estimated for cold start and hot stabilized running emission processes. Fleet-average profiles are calculated to include emissions from deteriorated high-emitting vehicles that are expected to continue to contribute disproportionately to the fleet-wide PM_2.5 emissions into the future. The profiles are calculated using a weighted average of the PM_2.5 composition according to the contribution of PM_2.5 emissions from each class of vehicles in the on-road gasoline fleet in the Kansas City Metropolitan Statistical Area. The paper introduces methods to exclude insignificant measurements, correct for organic carbon positive artifact, and control for contamination from the testing infrastructure in developing speciation profiles. The uncertainty of the PM_2.5 species fraction in each profile is quantified using sampling survey analysis methods. The primary use of the profiles is to develop PM_2.5 emissions inventories for the United States, but the profiles may also be used in source apportionment, atmospheric modeling, and exposure assessment, and as a basis for light-duty gasoline emission profiles for countries with limited data.

Implications: PM_2.5 speciation profiles were developed from a large sample of light-duty gasoline vehicles tested in the Kansas City area. Separate PM_2.5 profiles represent cold start and hot stabilized running emission processes to distinguish important differences in chemical composition. Statistical analysis was used to construct profiles that represent PM_2.5 emissions from the U.S. vehicle fleet based on vehicles tested from the 2005 calendar year Kansas City metropolitan area. The profiles have been incorporated into the EPA MOVES emissions model, as well as the EPA SPECIATE database, to improve emission inventories and provide the PM_2.5 chemical characterization needed by CMAQv5.0 for atmospheric chemistry modeling.     

Vertical emission profiles for Europe based on plume rise calculations

The vertical allocation of emissions has a major impact on results of Chemistry Transport Models. However, in Europe it is still common to use fixed vertical profiles based on rough estimates to determine the emission height of point sources. This publication introduces a set of new vertical profiles for the use in chemistry transport modeling that were created from hourly gridded emissions calculated by the SMOKE for Europe emission model. SMOKE uses plume rise calculations to determine effective emission heights. Out of more than 40 000 different vertical emission profiles 73 have been chosen by means of hierarchical cluster analysis. These profiles show large differences to those currently used in many emission models. Emissions from combustion processes are released in much lower altitudes while those from production processes are allocated to higher altitudes. The profiles have a high temporal and spatial variability which is not represented by currently used profiles.     

Modeling an air pollution episode in northwestern United States: Identifying the effect of nitrogen oxide and volatile organic compound emission changes on air pollutants formation using direct sensitivity analysis

Air quality impacts of volatile organic compound (VOC) and nitrogen oxide (NO_x) emissions from major sources over the northwestern United States are simulated. The comprehensive nested modeling system comprises three models: Community Multiscale Air Quality (CMAQ), Weather Research and Forecasting (WRF), and Sparse Matrix Operator Kernel Emissions (SMOKE). In addition, the decoupled direct method in three dimensions (DDM-3D) is used to determine the sensitivities of pollutant concentrations to changes in precursor emissions during a severe smog episode in July of 2006. The average simulated 8-hr daily maximum O₃ concentration is 48.9 ppb, with 1-hr O₃ maxima up to 106 ppb (40 km southeast of Seattle). The average simulated PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm) concentration at the measurement sites is 9.06 μg m^?3, which is in good agreement with the observed concentration (8.06 μg m^?3). In urban areas (i.e., Seattle, Vancouver, etc.), the model predicts that, on average, a reduction of NO_x emissions is simulated to lead to an increase in average 8-hr daily maximum O₃ concentrations, and will be most prominent in Seattle (where the greatest sensitivity is??0.2 ppb per % change of mobile sources). On the other hand, decreasing NO_x emissions is simulated to decrease the 8-hr maximum O₃ concentrations in remote and forested areas. Decreased NO_x emissions are simulated to slightly increase PM_2.5 in major urban areas. In urban areas, a decrease in VOC emissions will result in a decrease of 8-hr maximum O₃ concentrations. The impact of decreased VOC emissions from biogenic, mobile, nonroad, and area sources on average 8-hr daily maximum O₃ concentrations is up to 0.05 ppb decrease per % of emission change, each. Decreased emissions of VOCs decrease average PM_2.5 concentrations in the entire modeling domain. In major cities, PM_2.5 concentrations are more sensitive to emissions of VOCs from biogenic sources than other sources of VOCs. These results can be used to interpret the effectiveness of VOC or NO_x controls over pollutant concentrations, especially for localities that may exceed National Ambient Air Quality Standards (NAAQS).

Implications: The effect of NO_x and VOC controls on ozone and PM_2.5 concentrations in the northwestern United States is examined using the decoupled direct method in three dimensions (DDM-3D) in a state-of-the-art three-dimensional chemical transport model (CMAQ). NO_x controls are predicted to increase PM_2.5 and ozone in major urban areas and decrease ozone in more remote and forested areas. VOC reductions are helpful in reducing ozone and PM_2.5 concentrations in urban areas. Biogenic VOC sources have the largest impact on O₃ and PM_2.5 concentrations.     

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 area's 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.     

Photochemical model performance for PM2.5 sulfate,nitrate, ammonium,and precursor species SO2, HNO3, and NH3 at background monitor locations in the central and eastern United States

Health studies have shown premature death is statistically associated with exposure to particulate matter <2.5 μm in diameter (PM2.5). The United States Environmental Protection Agency requires all States with PM2.5 non-attainment counties or with sources contributing to visibility impairment at Class I areas to submit an emissions control plan. These emission control plans will likely focus on reducing emissions of sulfur oxides and nitrogen oxides, which form two of the largest chemical components of PM2.5 in the eastern United States: ammonium sulfate and ammonium nitrate. Emission control strategies are simulated using three-dimensional Eulerian photochemical transport models.A monitor study was established using one urban (Detroit) and nine rural locations in the central and eastern United States to simultaneously measure PM2.5 sulfate ion (SO₄²⁻), nitrate ion (NO₃⁻), ammonium ion (NH₄⁺), and precursor species sulfur dioxide (SO₂), nitric acid (HNO₃), and ammonia (NH₃). This monitor study provides a unique opportunity to assess how well the modeling system predicts the spatial and temporal variability of important precursor species and co-located PM2.5 ions, which is not well characterized in the central and eastern United States.The modeling system performs well at estimating the PM2.5 species, but does not perform quite as well for the precursor species. Ammonia is under-predicted in the coldest months, nitric acid tends to be over-predicted in the summer months, and sulfur dioxide appears to be systematically over-predicted. Several indicators of PM2.5 ammonium sulfate and ammonium nitrate formation and chemical composition are estimated with the ambient data and photochemical model output. PM2.5 sulfate ion is usually not fully neutralized to ammonium sulfate in ambient measurements and is usually fully neutralized in model estimates. The model and ambient estimates agree that the ammonia study monitors tend to be nitric acid limited for PM2.5 nitrate formation. Regulatory strategies in this part of the country should focus on reductions in NO_X rather than ammonia to control PM2.5 ammonium nitrate.     

Preliminary evaluation of the Community Multiscale Air Quality model for 2002 over the Southeastern United States

The Visibility Improvement State and Tribal Association of the Southeast (VISTAS) is one of five Regional Planning Organizations that is charged with the management of haze, visibility, and other regional air quality issues in the United States. The VISTAS Phase I work effort modeled three episodes (January 2002, July 1999, and July 2001) to identify the optimal model configuration(s) to be used for the 2002 annual modeling in Phase II. Using model configurations recommended in the Phase I analysis, 2002 annual meteorological (Mesoscale Meterological Model [MM5]), emissions (Sparse Matrix Operator Kernal Emissions [SMOKE]), and air quality (Community Multiscale Air Quality [CMAQ]) simulations were performed on a 36-km grid covering the continental United States and a 12-km grid covering the Eastern United States. Model estimates were then compared against observations. This paper presents the results of the preliminary CMAQ model performance evaluation for the initial 2002 annual base case simulation. Model performance is presented for the Eastern United States using speciated fine particle concentration and wet deposition measurements from several monitoring networks. Initial results indicate fairly good performance for sulfate with fractional bias values generally within +/-20%. Nitrate is overestimated in the winter by approximately +50% and underestimated in the summer by more than -100%. Organic carbon exhibits a large summer underestimation bias of approximately -100% with much improved performance seen in the winter with a bias near zero. Performance for elemental carbon is reasonable with fractional bias values within +/- 40%. Other fine particulate (soil) and coarse particular matter exhibit large (80-150%) overestimation in the winter but improved performance in the summer. The preliminary 2002 CMAQ runs identified several areas of enhancements to improve model performance, including revised temporal allocation factors for ammonia emissions to improve nitrate performance and addressing missing processes in the secondary organic aerosol module to improve OC performance.     

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.     

Understanding the Effectiveness of Precursor Reductions in Lowering 8-Hr Ozone Concentrations—Part II. The Eastern United States

Abstract

Analyses of ozone (O₃) measurements in conjunction with photochemical modeling were used to assess the feasibility of attaining the federal 8-hr O₃ standard in the eastern United States. Various combinations of volatile organic compound (VOC) and oxides of nitrogen (NO_x) emission reductions were effective in lowering modeled peak 1-hr O₃ concentrations. VOC emissions reductions alone had only a modest impact on modeled peak 8-hr O₃ concentrations. Anthropogenic NO_x emissions reductions of 46–86% of 1996 base case values were needed to reach the level of the 8-hr standard in some areas. As NO_x emissions are reduced, O₃ production efficiency increases, which accounts for the less than proportional response of calculated 8-hr O₃ levels. Such increases in O₃ production efficiency also were noted in previous modeling work for central California. O₃ production in some urban core areas, such as New York City and Chicago, IL, was found to be VOC-limited. In these areas, moderate NO_x emissions reductions may be accompanied by increases in peak 8-hr O₃ levels. The findings help to explain differences in historical trends in 1- and 8-hr O₃ levels and have serious implications for the feasibility of attaining the 8-hr O₃ standard in several areas of the eastern United States.     

Regional NOx emission inversion through a four-dimensional variational approach using SCIAMACHY tropospheric NO2 column observations

In this paper, the NOx emission scaling factors applied over the 2001 National Emissions Inventory (NEI) are estimated through a four-dimensional variational (4D-Var) approach using SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY) tropospheric NO₂ columns measured during summer 2004. In the “top-down” approach, two-month average NO₂ columns are assimilated into a regional chemical transport model (CTM), STEM, using different assimilation setups. In a basic setup, NOx emissions are adjusted by assimilating the NO₂ columns. A more general setup of emission inversion allows the initial O₃ concentrations be adjusted along with the NOx emissions. A final case is set up to assimilate both the NO₂ columns and O₃ measurement from various platforms while allowing adjustments of both the NOx emissions and the initial O₃ concentrations. It is found that the addition of O₃ measurements did not improve the NOx emission inversion. With the NOx emission at surface and upper levels being adjusted separately, results from four cases show that the elevated NOx emission reduction ranges from 8.9% to 11.4%, and the surface NOx emission reduction is up to 6.6%. All the cases show NOx emission reduction in Ohio valley and Washington, District of Columbia areas.     

Air Quality Modeling of Interpollutant Trading for Ozone Precursors in an Urban Area

Abstract

Emission trading is a market‐based approach designed to improve the efficiency and economic viability of emission control programs; emission trading has typically been confined to trades among single pollutants. Interpollutant trading (IPT), as described in this work, allows for trades among emissions of different compounds that affect the same air quality end point, in this work, ambient ozone (O₃) concentrations. Because emissions of different compounds impact air quality end points differently, weighting factors or trading ratios (tons of emissions of nitrogen oxides (NO_x) equivalent to a ton of emissions of volatile organic compounds [VOCs]) must be developed to allow for IPT. In this work, IPT indices based on reductions in O₃ concentrations and based on reductions in population exposures to O₃ were developed and evaluated using a three‐dimensional gridded photochemical model for Austin, TX, a city currently on the cusp of nonattainment with the National Ambient Air Quality Standards for O₃ concentrations averaged over 8 hr. Emissions of VOC and NO_x from area and mobile sources in Austin are larger than emissions from point sources. The analysis indicated that mobile and area sources exhibited similar impacts. Trading ratios based on maximum O₃ concentration or population exposure were similar. In contrast, the trading ratios did exhibit significant (more than a factor of two) day‐to‐day variability. Analysis of the air quality modeling indicated that the daily variability in trading ratios could be attributed to daily variations in both emissions and meteorology.     

Control of Ozone Precursors in a Complex Industrial Terrain by Using Multiscale-Nested Air Quality Models with Fine Spatial Resolution (1 km2)

Abstract

The location of the northeastern Iberian Peninsula (NEIP) in the northwestern Mediterranean basin, the presence of the Pyrenees mountain range (with altitudes >3000 m), and the influence of the Mediterranean Sea and the large valley canalization of Ebro river induce an extremely complicated structure for the dispersion of photochemical pollutants. Air pollution studies in very complex terrains such as the NEIP require high-resolution modeling for resolving the very complex dynamics of flows. To deal with the influence of larger-scale transport, however, high-resolution models have to be nested in larger models to generate appropriate initial and boundary conditions for the finer resolution domains. This article shows the results obtained through the utilization of the MM5-EMICAT2000-CMAQ multiscale-nested air quality model relating the sensitivity regimes for ozone (O₃)-nitrogen oxides (NO_x)-volatile organic compounds (VOCs) in an area of high geographical complexity, like the industrial area of Tarragona, located in the NEIP. The model was applied with fine temporal (one-hour) and spatial resolution (cells of 24 km, 2 km, and 1 km) to represent the chemistry and transport of tropospheric O₃ and other photochemical species with respect to different hypothetical scenarios of emission controls and to quantify the influence of different emission sources in the area. Results indicate that O₃ chemistry in the industrial domain of Tarragona is strongly sensitive to VOCs; the higher percentages of reduction for ground-level O₃ are achieved when reducing by 25% the emissions of industrial VOCs. On the contrary, reductions in the industrial emissions of NO_x contribute to a strong increase in hourly peak levels of O₃. At the same time, the contribution of on-road traffic and biogenic emissions to ground-level O₃ concentrations in the area is negligible with respect to the pervasive weight of industrial sources. This analysis provides an assessment of the effectiveness of different policies for the control of emission of precursors by comparing the modeled results for different scenarios.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Air Quality Impacts of Distributed Energy Resources Implemented in the Northeastern United States

Abstract

Emissions from the potential installation of distributed energy resources (DER) in the place of current utility-scale power generators have been introduced into an emissions inventory of the northeastern United States. A methodology for predicting future market penetration of DER that considers economics and emission factors was used to estimate the most likely implementation of DER. The methodology results in spatially and temporally resolved emission profiles of criteria pollutants that are subsequently introduced into a detailed atmospheric chemistry and transport model of the region. The DER technology determined by the methodology includes 62% reciprocating engines, 34% gas turbines, and 4% fuel cells and other emerging technologies. The introduction of DER leads to retirement of 2625 MW of existing power plants for which emissions are removed from the inventory. The air quality model predicts maximum differences in air pollutant concentrations that are located downwind from the central power plants that were removed from the domain. Maximum decreases in hourly peak ozone concentrations due to DER use are 10 ppb and are located over the state of New Jersey. Maximum decreases in 24-hr average fine particulate matter (PM_2.5) concentrations reach 3 μg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM_2.5 is the reduction of sulfate levels due to significant reductions in direct emissions of sulfur oxides (SO_x) from the DER compared with the central power plants removed. The scenario presented here represents an accelerated DER penetration case with aggressive emission reductions due to removal of highly emitting power plants. Such scenario provides an upper bound for air quality benefits of DER implementation scenarios.     

Particulate Emissions from Construction Activities

Abstract

Although it has long been recognized that road and building construction activity constitutes an important source of particulate matter (PM) emissions throughout the United States, until recently only limited research has been directed to its characterization. This paper presents the results of PM₁₀ and PM_2.5 (particles ≤10 μm and ≤2.5 μm in aerodynamic diameter, respectively) emission factor development from the onsite testing of component operations at actual construction sites during the period 1998 –2001. Much of the testing effort was directed at earthmoving operations with scrapers, because earthmoving is the most important contributor of PM emissions across the construction industry. Other sources tested were truck loading and dumping of crushed rock and mud and dirt carryout from construction site access points onto adjacent public paved roads. Also tested were the effects of watering for control of scraper travel routes and the use of paved and graveled aprons at construction site access points for reducing mud and dirt carryout. The PM₁₀ emissions from earthmoving were found to be up to an order of magnitude greater than predicted by AP-42 emission factors drawn from other industries. As expected, the observed PM_2.5:PM₁₀ emission factor ratios reflected the relative importance of the vehicle exhaust and the resuspended dust components of each type of construction activity. An unexpected finding was that PM_2.5 emissions from mud and dirt carryout were much less than anticipated. Finally, the control efficiency of watering of scraper travel routes was found to closely follow a bilinear moisture model.     

Identification of sources of atmospheric PM at the Pittsburgh Supersite—Part III: Source characterization

Single-particle mass spectrometry data collected during the Pittsburgh Supersite experiment was used to isolate an episode on 27 October 2001 when the measurement site was primarily influenced by emissions from coal combustion sources. Results showed that (a) 60–80% of the particles detected during this event belonged to the Na/Si/K/Ca/Fe/Ga/Pb particle class associated with coal combustion emissions, (b) observation of this class was an isolated event occurring only during the hours of 06:00–14:00 EST, and (c) the detection of these particles was highly correlated with shifts in wind direction. Coincident SMPS, TEOM PM_2.5, SO₂, NO_x, and O₃ measurements were in excellent agreement with the single-particle results in terms of both identifying and characterizing this event. The three most likely point sources of these particles were isolated and Gaussian plume dispersion models were used in reverse to predict their particle number, particle mass, and gas phase emissions. Calculated mass emission rates were in general agreement with the US EPA National Emissions Inventory (NEI) database emissions estimates and the Title V PM₁₀ limit. The largest of the three sources emits about 2.4×10¹⁷ fine and ultrafine particles per second.     

An extended approach to calculate the ozone relative response factors used in the attainment demonstration for the National Ambient Air Quality Standards

With the promulgation of the National Ambient Air Quality Standards (NAAQS or standard) for 8-hr ozone (O₃), the U.S. Environmental Protection Agency (EPA) issued modeling guidance that advocated the use of results from photochemical air quality models in a relative sense. In doing so, the EPA provided guidance on how to calculate relative response factors (RRFs) that can project current design value (DV) mixing ratios into the future for the purpose of determining the attainment status with respect to the O₃ standard. The RRFs recommended by the EPA represent the average response of the photochemical model over a broad range of O₃ mixing ratios above a specified cutoff threshold. However, it is known that O₃ response to emission reductions of limiting precursors (i.e., NO_x and/or VOC) is greater on days with higher O₃ mixing ratios compared to days with lower mixing ratios. In this study, we present a segmented RRF concept termed band-RRF, which takes into account the different model responses at different O₃ mixing ratios. The new band-RRF concept is demonstrated in the San Joaquin Valley (SJV) region of California for the 1-hr and 8-hr O₃ standards. The 1-hr O₃ analysis is relevant to work done in support of the SJV O₃ State Implementation Plan (SIP) submitted to the EPA in 2013. The 8-hr example for the future year of 2019 is presented for illustrative purposes only. Further work will be conducted with attainment deadline of 2032 as part of upcoming SIPs for the 0.075 parts per million (ppm) 8-hr O₃ standard. The applicability of the band-RRF concept to the particulate matter (PM_2.5) standards is also discussed.

Implications:Results of photochemical models are used in regulatory applications in a relative sense using relative response factors (RRFs), which represent the impacts of emissions reductions over a wide range of ozone (O₃) values. It is possible to extend the concept of RRFs to account for the fact that higher O₃ mixing ratios (both 1-hr and 8-hr) respond more to emissions controls of limiting precursors than do lower O₃ mixing ratios. We demonstrate this extended concept, termed band-RRF, for the 1-hr and 8-hr O₃ National Ambient Air Quality Standard (NAAQS or standard) in the San Joaquin Valley of California. This extension can also be made applicable to the 24-hr PM_2.5 and annual PM_2.5 standards.     

A Fuel-Based Assessment of Off-Road Diesel Engine Emissions

ABSTRACT

The use of diesel engines in off-road applications is a significant source of nitrogen oxides (NO_x) and particulate matter (PM₁₀). Such off-road applications include railroad locomotives, marine vessels, and equipment used for agriculture, construction, logging, and mining. Emissions from these sources are only beginning to be controlled. Due to the large number of these engines and their wide range of applications, total activity and emissions from these sources are uncertain. A method for estimating the emissions from off-road diesel engines based on the quantity of diesel fuel consumed is presented. Emission factors are normalized by fuel consumption, and total activity is estimated by the total fuel consumed.

Total exhaust emissions from off-road diesel equipment (excluding locomotives and marine vessels) in the United States during 1996 have been estimated to be 1.2 × 10⁹ kg NO_x and 1.2 x 10⁸ kg PM₁₀. Emissions estimates published by the U.S. Environmental Protection Agency are 2.3 times higher for both NO_x and exhaust PM₁₀ emissions than estimates based directly on fuel consumption. These emissions estimates disagree mainly due to differences in activity estimates, rather than to differences in the emission factors. All current emission inventories for off-road engines are uncertain because of the limited in-use emissions testing that has been performed on these engines. Regional- and state-level breakdowns in diesel fuel consumption by off-road mobile sources are also presented. Taken together with on-road measurements of diesel engine emissions, results of this study suggest that in 1996, off-road diesel equipment (including     

Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emission factors for practical street pollution modelling

In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. This paper compares current available emission factor estimates for PM₁₀ and PM_2.5 from emission databases and different emission models, and validates these against eight high quality street pollution measurements in Denmark, Sweden, Germany, Finland and Austria.The data sets show large variation of the PM concentration and emission factors with season and with location. Consistently at all roads the PM₁₀ and PM_2.5 emission factors are lower in the summer month than the rest of the year. For example, PM₁₀ emission factors are in average 5–45% lower during the month 6–10 compared to the annual average.The range of observed total emission factors (including non-exhaust emissions) for the different sites during summer conditions are 80–130 mg km⁻¹ for PM₁₀, 30–60 mg km⁻¹ for PM_2.5 and 20–50 mg km⁻¹ for the exhaust emissions.We present two different strategies regarding modelling of PM emissions: (1) For Nordic conditions with strong seasonal variations due to studded tyres and the use of sand/salt as anti-skid treatment a time varying emission model is needed. An empirical model accounting for these Nordic conditions was previously developed in Sweden. (2) For other roads with a less pronounced seasonal variation (e.g. in Denmark, Germany, Austria) methods using a constant emission factor maybe appropriate. Two models are presented here.Further, we apply the different emission models to data sets outside the original countries. For example, we apply the “Swedish” model for two streets without studded tyre usage and the “German” model for Nordic data sets. The “Swedish” empirical model performs best for streets with studded tyre use, but was not able to improve the correlation versus measurements in comparison to using constant emission factors for the Danish side. The “German” method performed well for the streets without clear seasonal variation and reproduces the summer conditions for streets with pronounced seasonal variation. However, the seasonal variation of PM emission factors can be important even for countries not using studded tyres, e.g. in areas with cold weather and snow events using sand and de-icing materials. Here a constant emission factor probably will under-estimate the 90-percentiles and therefore a time varying emission model need to be used or developed for such areas.All emission factor models consistently indicate that a large part (about 50–85% depending on the location) of the total PM₁₀ emissions originates from non-exhaust emissions. This implies that reduction measures for the exhaust part of the vehicle emissions will only have a limited effect on ambient PM₁₀ levels.