Quantification of hourly variability in NO(x) emissions for baseload coal-fired power plants

The objectives of this paper are to (1) quantify variability in hourly utility oxides of nitrogen (NO(x)) emission factors, activity factors, and total emissions; (2) investigate the autocorrelation structure and evaluate cyclic effects at short and long scales of the time series of total hourly emissions; (3) compare emissions for the ozone (O3) season versus the entire year to identify seasonal differences, if any; and (4) evaluate interannual variability. Continuous emissions monitoring data were analyzed for 1995 and 1998 for 32 units from nine baseload power plants in the Charlotte, NC, airshed. Unit emissions have a strong 24-hr cycle attributable primarily to the capacity factor. Typical ranges of the coefficient of variation for emissions at a given hour of the day were from 0.2 to 0.45. Little difference was found when comparing weekend emissions with the entire week or when comparing the O3 season with the entire year. There were substantial differences in the mean and standard deviation of emissions when comparing 1995 and 1998 data, indicative of the effect of retrofits of control technology during the intervening time. The wide range of variability and its autocorrelation should be accounted for when developing probabilistic utility emission inventories for analysis of near-term future episodes.     

A comparison of hourly with annual air pollutant emissions: Implications for estimating acute exposure and public health risk

Health risks from air pollutants are evaluated by comparing chronic (i.e., an average over 1 yr or greater) or acute (typically 1-hr) exposure estimates with chemical- and duration-specific reference values or standards. When estimating long-term pollutant concentrations via exposure modeling, facility-level annual average emission rates are readily available as model inputs for most air pollutants. In contrast, there are far fewer facility-level hour-by-hour emission rates available for many of these same pollutants. In this report, we first analyze hour-by-hour emission rates for total reduced sulfur (TRS) compounds from eight kraft pulp mill operations. This data set is used to demonstrate discrepancies between estimating exposure based on a single TRS emission rate that has been calculated as the mean of all operating hours of the year, as opposed to reported hourly emission rates. A similar analysis is then performed using reported hourly emission rates for sulfur dioxide (SO₂) and oxides of nitrogen (NO_x) from three power generating units from a U.S. power plant. Results demonstrate greater variability at kraft pulp mill operations, with ratios of reported hourly to average hourly TRS emissions ranging from less than 1 to greater than 160 during routine facility operations. Thus, if fluctuations in hourly emission rates are not accounted for, over- or underestimates of hourly exposure, and thus acute health risk, may occur. In addition to this analysis, we also demonstrate an additional challenge when assessing health risk based on hourly exposures: the lack of human health reference values based on 1-hr exposures.

Implications: Largely due to the lack of reported hourly emission rate data for many air pollutants, an hourly average emission rate (calculated from an annual emission rate) is often used when modeling the potential for acute health risk. We calculated ratios between reported hourly and hourly average emission rates from pulp and paper mills and a U.S. power plant to demonstrate that if not considered, hourly fluctuations in emissions could result in an over- or underestimation of exposure and risk. We also demonstrate the lack of 1-hr human health reference values meant to be protective of the general population, including children.     

An Environmental Decision-Making Tool for Evaluating Ground-Level Ozone-Related Health Effects

Abstract

A computer model called the Ozone Risk Assessment Model (ORAM) was developed to evaluate the health effects caused by ground-level ozone (O₃) exposure. ORAM was coupled with the U.S. Environmental Protection Agency’s (EPA) Third-Generation Community Multiscale Air Quality model (Models-3/CMAQ), the state-of-the-art air quality model that predicts O₃ concentration and allows the examination of various scenarios in which emission rates of O₃ precursors (basically, oxides of nitrogen [NO_x] and volatile organic compounds) are varied. The principal analyses in ORAM are exposure model performance evaluation, health-effects calculations (expected number of respiratory hospital admissions), economic valuation, and sensitivity and uncertainty analysis through a Monte Carlo simulation. As a demonstration of the system, ORAM was applied to the eastern Tennessee region, and the entire O₃ season was simulated for a base case (typical emissions) and three different emission scenarios. The results indicated that a synergism occurs when reductions in NO_x emissions from mobile and point sources were applied simultaneously. A 12.9% reduction in asthma hospital admissions is expected when both mobile and point source NO_x emissions are reduced (50 and 70%, respectively) versus a 5.8% reduction caused by mobile source and a 3.5% reduction caused by point sources when these emission sources are reduced individually.     

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.     

Emissions Variability Processor (EMVAP): Design,evaluation, and application

Emissions of pollutants such as SO₂ and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration predictions. This procedure uses emissions varied hourly with a Monte Carlo sampling process that is based upon the user-specified emissions distribution, from which a probabilistic estimate can be obtained of the controlling concentration. EMVAP can also accommodate an advanced Tier 2 NO₂ modeling technique that uses a varying ambient ratio method approach to determine the fraction of total oxides of nitrogen that are in the form of nitrogen dioxide. For the case of the 1-hr National Ambient Air Quality Standards (NAAQS, established for SO₂ and NO₂), a “critical value” can be defined as the highest hourly emission rate that would be simulated to satisfy the standard using air dispersion models assuming constant emissions throughout the simulation. The critical value can be used as the starting point for a procedure like EMVAP that evaluates the impact of emissions variability and uses this information to determine an appropriate value to use for a longer term (e.g., 30-day) average emission rate that would still provide protection for the NAAQS under consideration. This paper reports on the design of EMVAP and its evaluation on several field databases that demonstrate that EMVAP produces a suitably modest overestimation of design concentrations. We also provide an example of an EMVAP application that involves a case in which a new emission limitation needs to be considered for a hypothetical emission unit that has infrequent higher-than-normal SO₂ emissions.

ImplicationsEmissions of pollutants from combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup and shutdown. While monitoring will automatically reflect this variability on measured concentrations, dispersion modeling is typically conducted with a single peak emission rate assumed to occur continuously. To realistically account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, the authors have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates.     

Inhomogeneity of methane emissions from a dairy waste lagoon

Methane (CH₄) is the dominant greenhouse gas emitted by animal agriculture manure. Since the gas is relatively insoluble in water, it is concentrated in discrete bubbles that rise through waste lagoons and burst at the surface. This results in lagoon emissions that are inhomogeneous in both space and time. Emissions from a midwestern dairy waste lagoon were measured over 2 weeks to evaluate the spatial homogeneity of the source emissions and to compare two methods for measuring this inhomogeneous emission. Emissions were determined using an inverse dispersion model based on CH₄ concentrations measured both by a single scanning tunable diode laser (TDL) aimed at a series of reflectors and by flame ionization detection (FID) gas chromatography on line-sampled air. Emissions were best estimated using scanned TDL concentrations over relatively short optical paths that collectively span the entire cross-wind width of the source, so as to provide both the best capture of discrete plumes from the bursting bubbles on the lagoon surface and the best detection of CH₄ background concentrations. The lagoon emissions during the study were spatially inhomogeneous at hourly time scales. Partitioning the inhomogeneous source into two source regions reduced the estimated emissions of the overall lagoon by 57% but increased the variability. Consequently, it is important to assess the homogeneity of a source prior to measurements and final emissions calculation.

Implications: Plans for measuring methane emissions from waste lagoons must take into account the spatial inhomogeneity of the source strength. The assumption of emission source homogeneity for a low-solubility gas such as CH₄ emitted from an animal waste lagoon can result in significant emission overestimates. The entire breadth and length of the area source must be measured, preferably with multiple optical paths, for the detection of discrete plumes from the different emitting regions and for determining the background concentration. Other gases with similarly poor solubility in water may also require partitioning of the lagoon source area.     

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.     

Regression modelling of hourly NOx and NO2 concentrations in urban air in London

Based on hourly measurements of NO_x NO₂ and O₃ and meteorological data, an ordinary least squares (OLS) model and a first-order autocorrelation (AR) model were developed to analyse the regression and prediction of NO_x and NO₂ concentrations in London. Primary emissions and wind speed are the most important factors influencing NO_x concentrations; in addition to these two, reaction of NO with O₃ is also a major factor influencing NO₂ concentrations. The AR model resulted in high correlation coefficients (R > 0.95) for the NO_x and NO₂ regression based on a whole year's data, and is capable of predicting NO₂ (R = 0.83) and NO_x (R = 0.65) concentrations when the explanatory variables were available. The analysis of the structure of regression models by Principal Component Analysis (PCA) indicates that the regression models are stable. The results of the OLS model indicate that there was an exceptional NO₂ source, other than primary emission and reaction of NO with O₃, in the air pollution episode in London in December 1991.     

Cost of lower NOx emissions: Increased CO2 emissions from heavy-duty diesel engines

This paper highlights the effect of emissions regulations on in-use emissions from heavy-duty vehicles powered by different model year engines. More importantly, fuel economy data for pre- and post-consent decree engines are compared.The objective of this study was to determine the changes in brake-specific emissions of NO_x as a result of emission regulations, and to highlight the effect these have had on brake-specific CO₂ emission; hence, fuel consumption. For this study, in-use, on-road emission measurements were collected. Test vehicles were instrumented with a portable on-board tailpipe emissions measurement system, WVU's Mobile Emissions Measurement System, and were tested on specific routes, which included a mix of highway and city driving patterns, in order to collect engine operating conditions, vehicle speed, and in-use emission rates of CO₂ and NO_x. Comparison of on-road in-use emissions data suggests NO_x reductions as high as 80% and 45% compared to the US Federal Test Procedure and Not-to-Exceed standards for model year 1995–2002. However, the results indicate that the fuel consumption; hence, CO₂ emissions increased by approximately 10% over the same period, when the engines were operating in the Not-to-Exceed region.     

Development of a Speciated,Hourly, and Gridded Air Pollutants Emission Modeling System—A Case Study on the Precursors of Photochemical Smog in the Seoul Metropolitan Area,Korea

ABSTRACT

A speciated, hourly, and gridded air pollutants emission modeling system (SHEMS) was developed and applied in predicting hourly nitrogen dioxide (NO₂) and ozone (O₃) levels in the Seoul Metropolitan Area (SMA). The primary goal of the SHEMS was to produce a systemized emission inventory for air pollutants including ozone precursors for modeling air quality in urban areas.

The SHEMS is principally composed of three parts: (1) a pre-processor to process emission factors, activity levels, and spatial and temporal information using a geographical information system; (2) an emission model for each source type; and (3) a post-processor to produce report and input data for air quality models through database modeling. The source categories in SHEMS are point, area, mobile, natural, and other sources such as fugitive emissions. The emission database produced by SHEMS contains 22 inventoried compounds: sulfur dioxide, NO₂, carbon monoxide, and 19 speciated volatile organic compounds. To validate SHEMS, the emission data were tested with the Urban Airshed Model to predict NO₂ and O₃ concentrations in the SMA during selected episode days in 1994. The results turned out to be reliable in describing temporal variation and spatial distribution of those pollutants.     

Assessment of a regulatory model's performance relative to large spatial heterogeneity in observed ozone in Houston,Texas

In Houston, some of the highest measured 8-hr ozone (O₃) peaks are characterized by sudden increases in observed concentrations of at least 40 ppb in 1 hr, or 60 ppb in 2 hr. Measurements show that these large hourly changes appear at only a few monitors and span a narrow geographic area, suggesting a spatially heterogeneous field of O₃ concentrations. This study assessed whether a regulatory air quality model (AQM) can simulate this observed behavior. The AQM did not reproduce the magnitude or location of some of the highest observed hourly O₃ changes, and it also failed to capture the limited spatial extent. On days with measured large hourly changes in O₃ concentrations, the AQM predicted high O₃ over large regions of Houston, resulting in overpredictions at several monitors. This analysis shows that the model can make high O₃, but on these days the predicted spatial field suggests that the model had a different cause. Some observed large hourly changes in O₃ concentrations have been linked to random releases of industrial volatile organic compounds (VOCs). In the AQM emission inventory, there are several emission events when an industrial point source increases VOC emissions in excess of 10,000 mol/hr. One instance increased predicted downwind O₃ concentrations up to 25 ppb. These results show that the modeling system is responsive to a large VOC release, but the timing and location of the release, and meteorological conditions, are critical requirements. Attainment of the O₃ standard requires the use of observational data and AQM predictions. If the large observed hourly changes are indicative of a separate cause of high O₃, then the model may not include that cause, which might result in regulators enacting control strategies that could be ineffective.

Implications To show the attainment of the O₃ standard, the U.S. Environmental Protection Agency (EPA) requires the use of observations and model predictions under the assumption that simulations are capable of reproducing observed phenomena. The regulatory model is unable to reproduce observed behavior measured in the observational database. If the large observed hourly changes were indicative of a separate cause of high O₃, then the model would not include that cause. Inaccurate model predictions may prompt air quality regulators to enact control strategies that are effective in the modeling system, but prove ineffective in the real world.     

Improved land cover and emission factors for modeling biogenic volatile organic compounds emissions from Hong Kong

This paper describes a study of local biogenic volatile organic compounds (BVOC) emissions from the Hong Kong Special Administrative Region (HKSAR). An improved land cover and emission factor database was developed to estimate Hong Kong emissions using MEGAN, a BVOC emission model developed by Guenther et al. (2006). Field surveys of plant species composition and laboratory measurements of emission factors were combined with other data to improve existing land cover and emission factor data. The BVOC emissions from Hong Kong were calculated for 12 consecutive years from 1995 to 2006. For the year 2006, the total annual BVOC emissions were determined to be 12,400 metric tons or 9.82 × 10⁹ g C (BVOC carbon). Isoprene emission accounts for 72%, monoterpene emissions account for 8%, and other VOCs emissions account for the remaining 20%. As expected, seasonal variation results in a higher emission in the summer and a lower emission in the winter, with emission predominantly in day time. A high emission of isoprene occurs for regions, such as Lowest Forest-NT North, dominated by broadleaf trees. The spatial variation of total BVOC is similar to the isoprene spatial variation due to its high contribution. The year to year variability in emissions due to weather was small over the twelve-year period (?1.4%, 2006 to 1995 trendline), but an increasing trend in the annual variation due to an increase in forest land cover can be observed (+7%, 2006 to 1995 trendline). The results of this study demonstrate the importance of accurate land cover inputs for biogenic emission models and indicate that land cover change should be considered for these models.     

A comparative performance evaluation of the AURAMS and CMAQ air-quality modelling systems

A harmonized comparative performance evaluation of A Unified Regional Air-quality Modelling System (AURAMS) v1.3.1b and Community Multiscale Air Quality (CMAQ) v4.6 air-quality modelling systems was conducted on the same North American grid for July 2002 using the same emission inventories, emissions processor, and input meteorology.Comparison of AURAMS- and CMAQ-predicted O₃ concentrations against hourly surface measurement data showed a lower normalized mean bias (NMB) of 20.7% for AURAMS versus 46.4% for CMAQ. However, AURAMS and CMAQ had more similar normalized mean errors (NMEs) of 46.9% and 54.2%, respectively. Both models did similarly well in predicting daily 1-h O₃ maximums; however, AURAMS performed better in calculating daily minimums. CMAQ's poorer performance for O₃ is partly due to its inability to correctly predict nighttime lows.Total PM_2.5 hourly surface concentration was under-predicted by both AURAMS and CMAQ with NMBs of ?10.4% and ?65.2%, respectively. However, as with O₃, both models had similar NMEs of 68.0% and 70.6%, respectively. In general, AURAMS performance was better than CMAQ for all major PM_2.5 species except nitrate and elemental carbon. Both models significantly under-predicted total organic aerosols (TOAs), although the mean AURAMS concentration was over four times larger than CMAQ's. The under-prediction of TOA was partly due to the exclusion of forest-fire emissions. Sea-salt aerosol made up approximately 50.2% of the AURAMS total PM_2.5 surface concentration versus only 6.2% in CMAQ when averaged over all grid cells. When averaged over land cells only, sea-salt still contributed 13.9% to the total PM_2.5 mass in AURAMS versus 2.0% in CMAQ.     

Sulfur Dioxide Emissions and Market Effects under the Clean Air Act Acid Rain Program

ABSTRACT

The Clean Air Act Amendments of 1990 (CAAA90) established a national program to control sulfur dioxide (SO₂) emissions from electricity generation. CAAA90's market-based approach includes trading and banking of Soumissions allowances. We analyzed data describing electric utility SO₂ emissions in 1995, the first year of the program's Phase I, and market effects over the 1990-1995 period. Fuel switching and flue-gas desulfurization were the dominant means used in 1995 by targeted generators to reduce emissions to 51% of 1990 levels. Flue-gas desulfur-ization costs, emissions allowance prices, low-sulfur coal prices, and average sulfur contents of coals shipped to electric utilities declined over the 1990-1995 period. Projections indicate that 13-15 million allowances will have been banked during the program's Phase I, which ends in 1999, a quantity expected to last through the first decade of the program's stricter Phase II controls. In 1995, both allowance prices and SO₂ emissions were below pre-CAAA90 expectations. The reduction of SO₂ emissions beyond pre-CAAA90 expectations, combined with lower-than-expected allowance prices and declining compliance costs, can be viewed as a success for market-based environmental controls.     

Real-World Energy Use and Emission Rates for Idling Long-Haul Trucks and Selected Idle Reduction Technologies

Abstract

Long-haul freight trucks typically idle for 2000 or more hours per year, motivating interest in reducing idle fuel use and emissions using auxiliary power units (APUs) and shore-power (SP). Fuel-use rates are estimated based on electronic control unit (ECU) data for truck engines and measurements for APU engines. Engine emission factors were measured using a portable emission measurement system. Indirect emissions from SP were based on average utility grid emission factors. Base engine fuel use and APU and SP electrical load were analyzed for 20 trucks monitored for more than 1 yr during 2.76 million mi of activity within 42 U.S. states. The average base engine fuel use varied from 0.46 to 0.65 gal/hr. The average APU fuel use varied from 0.24 to 0.41 gal/hr. Fuel-use rates are typically lowest in mild weather, highest in hot or cold weather, and depend on engine speed (revolutions per minute [RPM]). Compared with the base engine, APU fuel use and emissions of carbon dioxide (CO₂) and sulfur dioxide (SO₂) are lower by 36–47%. Oxides of nitrogen (NO_x) emissions are lower by 80–90%. Reductions in particulate matter (PM), carbon monoxide (CO), and hydrocarbon emissions vary from approximately 10 to over 50%. SP leads to more substantial reductions, except for SO₂. The actual achievable reductions will be lower because only a fraction of base engine usage will be replaced by APUs, SP, or both. Recommendations are made for reducing base engine fuel use and emissions, accounting for variability in fuel use and emissions reductions, and further work to quantify real-world avoided fuel use and emissions.     

Multispecies remote sensing measurements of vehicle emissions on Sherman Way in Van Nuys,California

As part of the 2010 Van Nuys tunnel study, researchers from the University of Denver measured on-road fuel-specific light-duty vehicle emissions from nearly 13,000 vehicles on Sherman Way (0.4 miles west of the tunnel) in Van Nuys, California, with its multispecies Fuel Efficiency Automobile Test (FEAT) remote sensor a week ahead of the tunnel measurements. The remote sensing mean gram per kilogram carbon monoxide (CO), hydrocarbon (HC), and oxide of nitrogen (NO_x) measurements are 8.9% lower, 41% higher, and 24% higher than the tunnel measurements, respectively. The remote sensing CO/NO_x and HC/NO_x mass ratios are 28% lower and 20% higher than the comparable tunnel ratios. Comparisons with the historical tunnel measurements show large reductions in CO, HC, and NO_x over the past 23 yr, but little change in the HC/NO_x mass ratio since 1995. The fleet CO and HC emissions are increasingly dominated by a few gross emitters, with more than a third of the total emissions being contributed by less than 1% of the fleet. An example of this is a 1995 vehicle measured three times with an average HC emission of 419 g/kg fuel (two-stroke snowmobiles average 475 g/kg fuel), responsible for 4% of the total HC emissions. The 2008 economic downturn dramatically reduced the number of new vehicles entering the fleet, leading to an age increase (>1 model year) of the Sherman Way fleet that has increased the fleet's ammonia (NH₃) emissions. The mean NH₃ levels appear little changed from previous measurements collected in the Van Nuys tunnel in 1993. Comparisons between weekday and weekend data show few fleet differences, although the fraction of light-duty diesel vehicles decreased from the weekday (1.7%) to Saturday (1.2%) and Sunday (0.6%).

Implications: On-road remote sensing emission measurements of light-duty vehicles on Sherman Way in Van Nuys, California, show large historical emission reductions for CO and HC emissions despite an older fleet arising from the 2008 economic downturn. Fleet CO and HC emissions are increasingly dominated by a few gross emitters, with a single 1995 vehicle measured being responsible for 4% of the entire fleet's HC emissions. Finding and repairing and/or scrapping as little as 2% of the fleet would reduce on-road tailpipe emissions by as much as 50%. Ammonia emissions have locally increased with the increasing fleet age.     

Regional trends in wet deposition of sulfate in the United States and SO2 emissions from 1980 through 1995

Regional trends of seasonal and annual wet deposition and precipitation-weighted concentrations (PWCs) of sulfate in the United States over the period 1980–1995 were developed from monitoring data and scaled to a mean of unity. To reduce some effects of year to year climatological variability, the unitless regional deposition and PWC trends were averaged (hereafter termed CONCDEP). The SO₂ emissions data over the same period from the United States, Canada, and northern Mexico, aggregated by state and province, were weighted appropriately for each deposition region in turn to produce scaled trends of the emissions affecting each region. The emission-weighting factors, which were held constant year to year, were estimated by exercise of a regional transport model. The sulfate CONCDEP regional trends are generally similar to those of regionally weighted SO₂ emissions, although the latter trends are less steep and the former trends have more year to year variability. In eastern regions, sulfate CONCDEPs and SO₂ emissions patterns both generally show an initial decrease, an essentially trendless middle period, and a final decrease as reductions mandated by the Acid Rain Provisions of the 1990 Clean Air Act Amendments began. Linear regressions of regional sulfate CONCDEPs on corresponding regionally weighted SO₂ emissions produced statistically significant relationships in all regions. The analysis indicated that although regional sulfate CONCDEPs decreased relatively faster than did SO₂ emissions during the period in all regions except the Great Plains, in general the slopes were not significantly different from unity.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).     

Sub-region (district) and sector level SO2 and NOx emissions for India: assessment of inventories and mitigation flexibility

Sub-regional and sector level distribution of SO₂ and NO_x emissions inventories for India have been estimated for all the 466 Indian districts using base data for years 1990 and 1995. Although, national level emissions provide general guidelines for assessing mitigation alternatives, but significant regional and sectoral variability exist in Indian emissions. Districts reasonably capture this variability to a fine grid as 80% of these districts are smaller than 1°×1° resolution with 60% being smaller than even 1/2°×1/2°. Moreover, districts in India have well-established administrative and institutional mechanisms that would be useful for implementing and monitoring measures. District level emission estimates thus offer a finer regional scale inventory covering the combined interests of the scientific community and policy makers. The inventory assessment methodology adopted is similar to that prescribed by the Intergovernmental Panel on Climate Change (IPCC) for greenhouse gas (GHG) emissions. The sectoral decomposition at district level includes emissions from fossil fuel combustion, non-energy emissions from industrial activities and agriculture. Total SO₂ and NO_x emissions from India were 3542 and 2636 Gg, respectively (1990) and 4638 and 3462 Gg (1995) growing at annual rate of around 5.5%. The sectoral composition of SO₂ emissions indicates a predominance of electric power generation sector (46%). Power and transport sector emissions equally dominate NO_x emissions contributing nearly 30% each. However, majority of power plants are situated in predominantly rural districts while the latter are concentrated in large urban centers. Mitigation efforts for transport sector NO_x emissions would therefore be higher. The district level analysis indicates diverse spatial distribution with the top 5% emitting districts contributing 46.5 and 33.3% of total national SO₂ and NO_x emissions, respectively. This skewed emission pattern, with a few districts, sectors and point sources emitting significant SO₂ and NO_x, offers mitigation flexibility to policy makers for cost-effective mitigation.     

The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy

The sensitivity of the CHIMERE model to emission reduction scenarios on particulate matter PM2.5 and ozone (O₃) in Northern Italy is studied. The emissions of NO_x, PM2.5 SO₂, VOC or NH₃ were reduced by 50% for different source sectors for the Lombardy region, together with 5 additional scenarios to estimate the effect of local measures on improving the air quality for the Po valley area. Firstly, we evaluate the model performance by comparing calculated surface aerosol concentrations for the standard case (no emission reductions) with observations for January and June 2005. Calculated monthly mean PM10 concentrations are in general underestimated. For June, modelled PM10 concentrations slightly overestimate the measurements. Calculated monthly mean SO₄, NO₃^?, NH₄⁺ concentrations are in good agreement with the observations for January and June. Secondly, the model sensitivity of emission reduction scenarios on PM2.5 and O₃ calculated concentrations for the Po valley area is evaluated. The most effective scenarios to abate PM2.5 concentration are based on the SNAP2 (non-industrial combustion plants) and SNAP7 (road traffic) sectors, for which the NO_x and PM2.5 emissions are reduced by 50%. The number of days that the 2015 PM2.5 limit value of 25 μg m^?3 in Milan is exceeded by reducing primary PM2.5 and NO_x emissions for SNAP2 and 7 by 50%, does not change in January when compared to the standard case for the Milan area. It appears that 40% of the PM2.5 concentration in the greater Milan area is caused by the emissions surrounding the Lombardy region and from the model boundary conditions.This study also showed that a more effective pollutant reduction (emissions) per ton of pollutant reduced (concentrations) for the greater Milan area is obtained by reducing the primary PM2.5 emissions for SNAP7 by 50%. The most effective scenario on PM2.5 decrease for which precursor emissions are reduced is achieved by reducing SO₂ emissions by 50% for SNAP7.Our study showed that during summer time, the largest reductions in O₃ concentrations are achieved for SNAP7 emission reductions, when volatile organic compounds (VOCs) are reduced by 50%.