Evaluation of low wind modeling approaches for two tall-stack databases

The performance of the AERMOD air dispersion model under low wind speed conditions, especially for applications with only one level of meteorological data and no direct turbulence measurements or vertical temperature gradient observations, is the focus of this study. The analysis documented in this paper addresses evaluations for low wind conditions involving tall stack releases for which multiple years of concurrent emissions, meteorological data, and monitoring data are available. AERMOD was tested on two field-study databases involving several SO₂ monitors and hourly emissions data that had sub-hourly meteorological data (e.g., 10-min averages) available using several technical options: default mode, with various low wind speed beta options, and using the available sub-hourly meteorological data. These field study databases included (1) Mercer County, a North Dakota database featuring five SO₂ monitors within 10 km of the Dakota Gasification Company’s plant and the Antelope Valley Station power plant in an area of both flat and elevated terrain, and (2) a flat-terrain setting database with four SO₂ monitors within 6 km of the Gibson Generating Station in southwest Indiana. Both sites featured regionally representative 10-m meteorological databases, with no significant terrain obstacles between the meteorological site and the emission sources. The low wind beta options show improvement in model performance helping to reduce some of the overprediction biases currently present in AERMOD when run with regulatory default options. The overall findings with the low wind speed testing on these tall stack field-study databases indicate that AERMOD low wind speed options have a minor effect for flat terrain locations, but can have a significant effect for elevated terrain locations. The performance of AERMOD using low wind speed options leads to improved consistency of meteorological conditions associated with the highest observed and predicted concentration events. The available sub-hourly modeling results using the Sub-Hourly AERMOD Run Procedure (SHARP) are relatively unbiased and show that this alternative approach should be seriously considered to address situations dominated by low-wind meander conditions.

Implications: AERMOD was evaluated with two tall stack databases (in North Dakota and Indiana) in areas of both flat and elevated terrain. AERMOD cases included the regulatory default mode, low wind speed beta options, and use of the Sub-Hourly AERMOD Run Procedure (SHARP). The low wind beta options show improvement in model performance (especially in higher terrain areas), helping to reduce some of the overprediction biases currently present in regulatory default AERMOD. The SHARP results are relatively unbiased and show that this approach should be seriously considered to address situations dominated by low-wind meander conditions.     

Combined analysis of modeled and monitored SO2 concentrations at a complex smelting facility

Vale Canada Limited owns and operates a large nickel smelting facility located in Sudbury, Ontario. This is a complex facility with many sources of SO₂ emissions, including a mix of source types ranging from passive building roof vents to North America's tallest stack. In addition, as this facility performs batch operations, there is significant variability in the emission rates depending on the operations that are occurring. Although SO₂ emission rates for many of the sources have been measured by source testing, the reliability of these emission rates has not been tested from a dispersion modeling perspective. This facility is a significant source of SO₂ in the local region, making it critical that when modeling the emissions from this facility for regulatory or other purposes, that the resulting concentrations are representative of what would actually be measured or otherwise observed. To assess the accuracy of the modeling, a detailed analysis of modeled and monitored data for SO₂ at the facility was performed. A mobile SO₂ monitor sampled at five locations downwind of different source groups for different wind directions resulting in a total of 168 hr of valid data that could be used for the modeled to monitored results comparison. The facility was modeled in AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model) using site-specific meteorological data such that the modeled periods coincided with the same times as the monitored events. In addition, great effort was invested into estimating the actual SO₂ emission rates that would likely be occurring during each of the monitoring events. SO₂ concentrations were modeled for receptors around each monitoring location so that the modeled data could be directly compared with the monitored data. The modeled and monitored concentrations were compared and showed that there were no systematic biases in the modeled concentrations.

Implications:

This paper is a case study of a Combined Analysis of Modelled and Monitored Data (CAMM), which is an approach promulgated within air quality regulations in the Province of Ontario, Canada. Although combining dispersion models and monitoring data to estimate or refine estimates of source emission rates is not a new technique, this study shows how, with a high degree of rigor in the design of the monitoring and filtering of the data, it can be applied to a large industrial facility, with a variety of emission sources. The comparison of modeled and monitored SO₂ concentrations in this case study also provides an illustration of the AERMOD model performance for a large industrial complex with many sources, at short time scales in comparison with monitored data. Overall, this analysis demonstrated that the AERMOD model performed well.     

Minimum turbulence assumptions and u* and L estimation for dispersion models during low-wind stable conditions

The U.S. Environmental Protection Agency (EPA) short-distance dispersion model, AERMOD, has been shown to overpredict by a factor of as much as 10 when compared with observed concentrations from continuous releases at the Oak Ridge, TN (OR), and Idaho Falls, ID (IF), field experiments during stable periods when wind speeds often dropped below 1 m/sec. Some of this overprediction tendency can be reduced by revising AERMOD's meteorological preprocessor's parameterizations of the friction velocity, u _* , during low-wind stable conditions, thus increasing the calculated σ _v and σ _w and hence the lateral and vertical dispersion rates. Observations show that as the mean wind speed approaches zero at night, there is always significant σ _v and σ _w over time periods of 15 to 60 min, while standard Monin–Obukhov Similarity Theory (MOST) predicts that σ _v and σ _w will approach zero. This paper focuses on the u _* estimation methods and the minimum turbulence (σ _v and σ _w ) assumptions in AERMOD (beta option 4) and two widely used U.S. operational dispersion models, AERMOD (v12345) and SCICHEM. The U.S. EPA has provided results of its tests with the OR and IF data, with its base AERMOD version and its December 2012 modified versions, which assume adjustments to the low-wind u _* and increases in the minimum σ _v parameterization. SCICHEM has relatively small mean bias for both data sets. The revised AERMOD shows much less mean bias, agreeing more with SCICHEM.

Implications:

Suggestions are made for improvements to dispersion models such as AERMOD to correct overpredictions during light-wind stable conditions. Methods for estimating u_*, L, and the minimum turbulence parameters (σ_v and σ_w) are reviewed and compared. SCICHEM and the current operational version and an optional beta version (December 2012) of AERMOD are evaluated with tracer data from low-wind stable field experiments in Idaho Falls and Oak Ridge. It is seen that the operational version of AERMOD overpredicts by a factor of 2 to 10, while the optional beta version of AERMOD and SCICHEM have much less bias.     

Critical review of the building downwash algorithms in AERMOD

The only documentation on the building downwash algorithm in AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model), referred to as PRIME (Plume Rise Model Enhancements), is found in the 2000 A&WMA journal article by Schulman, Strimaitis and Scire. Recent field and wind tunnel studies have shown that AERMOD can overpredict concentrations by factors of 2 to 8 for certain building configurations. While a wind tunnel equivalent building dimension study (EBD) can be conducted to approximately correct the overprediction bias, past field and wind tunnel studies indicate that there are notable flaws in the PRIME building downwash theory. A detailed review of the theory supported by CFD (Computational Fluid Dynamics) and wind tunnel simulations of flow over simple rectangular buildings revealed the following serious theoretical flaws: enhanced turbulence in the building wake starting at the wrong longitudinal location; constant enhanced turbulence extending up to the wake height; constant initial enhanced turbulence in the building wake (does not vary with roughness or stability); discontinuities in the streamline calculations; and no method to account for streamlined or porous structures.

Implications: This paper documents theoretical and other problems in PRIME along with CFD simulations and wind tunnel observations that support these findings. Although AERMOD/PRIME may provide accurate and unbiased estimates (within a factor of 2) for some building configurations, a major review and update is needed so that accurate estimates can be obtained for other building configurations where significant overpredictions or underpredictions are common due to downwash effects. This will ensure that regulatory evaluations subject to dispersion modeling requirements can be based on an accurate model. Thus, it is imperative that the downwash theory in PRIME is corrected to improve model performance and ensure that the model better represents reality.     

A numerical model for the simulation of SO2 concentrations in the Kyongin region,Korea

A method for solving the non-reactive tracer continuity equation using a time splitting technique and a Galerkin technique with chapeau functions as finite elements for the horizontal advection has been developed and employed to simulate SO₂ concentrations in the Kyongin region in Korea for a synoptic case of high pollution potential days in autumn with the relatively strong southwesterly geostrophic wind at the 850 hPa pressure level. The paired comparisons between hourly observed and the simulated SO₂ concentrations are made to test the model performance. The result indicates that the present model simulates quite well horizontal distribution patterns of SO₂ concentration. However, the simulated concentrations depend largely on the emission rate, suggesting the importance of accurate source identification for the accurate simulation of the concentration field.     

Evaluation of an explicit NOx chemistry method in AERMOD

An explicit NO_x chemistry method has been implemented in AERMOD version 15181, ADMSM. The scheme has been evaluated by comparison with the methodologies currently recommended by the U.S. EPA for Tier 3 NO₂ calculations, that is, OLM and PVMRM2. Four data sets have been used for NO₂ chemistry method evaluation. Overall, ADMSM-modeled NO₂ concentrations show the most consistency with the AERMOD calculations of NO_x and the highest Index of Agreement; they are also on average lower than those of both OLM and PVMRM2. OLM shows little consistency with modeled NO_x concentrations and markedly overpredicts NO₂. PVMRM2 shows performance closer to that of ADMSM than OLM; however, its behavior is inconsistent with modeled NO_x in some cases and it has less good statistics for NO₂. The trend in model performance can be explained by examining the features particular to each chemistry method: OLM can be considered as a screening model as it calculates the upper bound of conversion from NO to NO₂ possible with the background O₃ concentration; PVMRM2 includes a much-improved estimate of in-plume O₃ but is otherwise similar to OLM, assuming instantaneous reaction of NO with O₃; and ADMSM allows for the rate of this reaction and also the photolysis of NO₂. Evaluation with additional data sets is needed to further clarify the relative performance of ADMSM and PVMRM2.

Implications: Extensive evaluation of the current AERMOD Tier 3 chemistry methods OLM and PVMRM2, alongside a new scheme that explicitly calculates the oxidation of NO by O₃ and the reverse photolytic reaction, shows that OLM consistently overpredicts NO₂ concentrations. PVMRM2 performs well in general, but there are some cases where this method overpredicts NO₂. The new explicit NO_x chemistry scheme, ADMSM, predicts NO₂ concentrations that are more consistent with both the modeled NO_x concentrations and the observations.     

A comparison study of three urban air pollution models

The predictions of three urban air pollution models with varying degrees of mathematical and computational complexities are compared against the hourly SO₂ ground-level concentrations observed on 10 winter nights of the RAPS experiment in St. Louis. The emphasis in this study is on the prediction of urban area source concentrations. Statistics for the paired comparison of predictions of each model with the observations are presented. The RAM and the ATDL model with stable diffusion coefficients overestimated the observed night-time concentrations. The results show that the performance of the ATDL model with near-neutral diffusion coefficients is comparable to the more sophisticated 3-D grid numerical model.     

Use of AERMOD to Determine a Hydrogen Sulfide Emission Factor for Swine Operations by Inverse Modeling

This study was conducted to determine both optimal settings applied to the plume dispersion model, AERMOD, and a scalable emission factor for accurately determining the spatial distribution of hydrogen sulfide concentrations in the vicinity of swine concentrated animal feeding operations (CAFOs). These operations emit hydrogen sulfide from both housing structures and waste lagoons. With ambient measurements made at 4 stations within 1 km of large swine CAFOs in Iowa, an inverse-modeling approach applied to AERMOD was used to determine hydrogen sulfide emission rates. CAFO buildings were treated as volume sources whereas nearby lagoons were modeled as area sources. The robust highest concentration (RHC), calculated for both measured and modeled concentrations, was used as the metric for adjusting the emission rate until the ratio of the two RHC levels was unity. Utilizing this approach, an average emission flux rate of 0.57 μg/m(2)-s was determined for swine CAFO lagoons. Using the average total animal weight (kg) of each CAFO, an average emission factor of 6.06 × 10(-7) μg/yr-m(2)-kg was calculated. From studies that measured either building or lagoon emission flux rates, building fluxes, on a floor area basis, were considered equal to lagoon flux rates. The emission factor was applied to all CAFOs surrounding the original 4 sites and surrounding an additional 6 sites in Iowa, producing an average modeled-to-measured RHC ratio of 1.24. When the emission factor was applied to AERMOD to simulate the spatial distribution of hydrogen sulfide around a hypothetical large swine CAFO (1M kg), concentrations 0.5 km from the CAFO were 35 ppb and dropped to 2 ppb within 6 km of the CAFO. These values compare to a level of 30 ppb that has been determined by the State of Iowa as a threshold level for ambient hydrogen sulfide levels.     

Precision of atmospheric dry deposition data from the Clean Air Status and Trends Network

A collocated, dry deposition sampling program was begun in January 1987 by the US Environmental Protection Agency to provide ongoing estimates of the overall precision of dry deposition and supporting data entering the Clean Air Status and Trends Network (CASTNet) archive. Duplicate sets of dry deposition sampling instruments were installed adjacent to existing instruments and have been operated for various periods at 11 collocated field sites. All sampling and operations were performed using standard CASTNet procedures. The current study documents the bias-corrected precision of CASTNet data based on collocated measurements made at paired sampling sites representative of sites across the network. These precision estimates include the variability for all operations from sampling to data storage in the archive. Precision estimates are provided for hourly, instrumental ozone (O₃) concentration and meteorological measurements, hourly model estimates of deposition velocity (V_d) from collocated measurements of model inputs, hourly O₃ deposition estimates, weekly filter pack determinations of selected atmospheric chemical species, and weekly estimates of V_d and deposition for each monitored filter pack chemical species and O₃.Estimates of variability of weekly pollutant concentrations, expressed as coefficients of variation, depend on chemical species: NO₃⁻∼8.1%; HNO₃∼6.4%; SO₂∼4.3%; NH₄⁺∼3.7%; SO₄²⁻∼2.3%; and O₃∼1.3%. Precision of estimates of weekly V_d from collocated measurements of model inputs also depends on the chemical species: aerosols ∼2.8%; HNO₃∼2.6%; SO₂∼3.0%; and O₃∼2.0%. Corresponding precision of weekly deposition estimates are: NO₃⁻∼8.6%; HNO₃⁻∼5.2%; SO₂∼5.6%; NH₄⁺∼3.9%; SO₄²⁻∼3.5%; and O₃∼3.3%. Precision of weekly concentration, V_d estimates, and deposition estimates are comparable in magnitude and slightly smaller than the corresponding hourly values. Annual precision estimates, although uncertain due to their small sample size in the current study, are consistent with the corresponding weekly values.     

The development of sulfur dioxide and ozone exposure profiles that mimic ambient conditions in the rural southeastern united states

A design for constructing experimental mixed-pollutant exposure profiles that reflect regional O₃ and SO₂ ambient air quality is described. The profiles were developed using hour-by-hour O₃ and SO₂ concentration data from monitoring sites in the southeastern United States where slash pine is indigenous. Only sites designated rural or remote, with co-monitored O₃ and SO₂, and at least 75% of the hourly values reported for the period April– October, were used. Each site was characterized by concentration, frequency of occurrence and duration of concentration values, length of time between episodes, and frequency of cooccurrence. A base profile, a 30-day hour-by-hour concentration regime, was constructed using averaged air quality characteristics from the selected sites. Using the base profile, additional regimes were constructed by increasing the concentration of all hourly values above a designated minimum, or by increasing the frequency of occurrence of selected hourly concentrations.     

Field evaluations of newly available “interference-free” monitors for nitrogen dioxide and ozone at near-road and conventional National Ambient Air Quality Standards compliance sites

Long-standing measurement techniques for determining ground-level ozone (O₃) and nitrogen dioxide (NO₂) are known to be biased by interfering compounds that result in overestimates of high O₃ and NO₂ ambient concentrations under conducive conditions. An increasing near-ground O₃ gradient (NGOG) with increasing height above ground level is also known to exist. Both the interference bias and NGOG were investigated by comparing data from a conventional Federal Equivalent Method (FEM) O₃ photometer and an identical monitor upgraded with an “interference-free” nitric oxide O₃ scrubber that alternatively sampled at 2 m and 6.2 m inlet heights above ground level (AGL). Intercomparison was also made between a conventional nitrogen oxide (NO_x) chemiluminescence Federal Reference Method (FRM) monitor and a new “direct-measure” NO₂ NO_x 405 nm photometer at a near-road air quality measurement site. Results indicate that the O₃ monitor with the upgraded scrubber recorded lower regulatory-oriented concentrations than the deployed conventional metal oxide–scrubbed monitor and that O₃ concentrations 6.2 m AGL were higher than concentrations 2.0 m AGL, the nominal nose height of outdoor populations. Also, a new direct-measure NO₂ photometer recorded generally lower NO₂ regulatory-oriented concentrations than the conventional FRM chemiluminescence monitor, reporting lower daily maximum hourly average concentrations than the conventional monitor about 3 of every 5 days.

Implications: Employing bias-prone instruments for measurement of ambient ozone or nitrogen dioxide from inlets at inappropriate heights above ground level may result in collection of positively biased data. This paper discusses tests of new regulatory instruments, recent developments in bias-free ozone and nitrogen dioxide measurement technology, and the presence/extent of a near-ground O₃ gradient (NGOG). Collection of unbiased monitor inlet height–appropriate data is crucial for determining accurate design values and meeting National Ambient Air Quality Standards.     

AERCOARE: An overwater meteorological preprocessor for AERMOD

AERCOARE is a meteorological data preprocessor for the American Meteorological Society and U.S Environmental Protection Agency (EPA) Regulatory Model (AERMOD). AERCOARE includes algorithms developed during the Coupled-Ocean Atmosphere Response Experiment (COARE) to predict surface energy fluxes and stability from routine overwater measurements. The COARE algorithm is described and the implementation in AERCOARE is presented. Model performance for the combined AERCOARE-AERMOD modeling approach was evaluated against tracer measurements from four overwater field studies. Relatively better model performance was found when lateral turbulence measurements were available and when several key input variables to AERMOD were constrained. Namely, requiring the mixed layer height to be greater than 25 m and not allowing the Monin Obukhov length to be less than 5 m improved model performance in low wind speed stable conditions. Several options for low wind speed dispersion in AERMOD also affected the model performance results. Model performance for the combined AERCOARE-AERMOD modeling approach was found to be comparable to the current EPA regulatory Offshore Coastal Model (OCD) for the same tracer studies. AERCOARE-AERMOD predictions were also compared to simulations using the California Puff-Advection Model (CALPUFF) that also includes the COARE algorithm. Many model performance measures were found to be similar, but CALPUFF had significantly less scatter and better performance for one of the four field studies. For many offshore regulatory applications, the combined AERCOARE-AERMOD modeling approach was found to be a viable alternative to OCD the currently recommended model.

Implications: A new meteorological preprocessor called AERCOARE was developed for offshore source dispersion modeling using the U.S. Environmental Protection Agency (EPA) regulatory model AERMOD. The combined AERCOARE-AERMOD modeling approach allows stakeholders to use the same dispersion model for both offshore and onshore applications. This approach could replace current regulatory practices involving two completely different modeling systems. As improvements and features are added to the dispersion model component, AERMOD, such techniques can now also be applied to offshore air quality permitting.     

Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance: Part I—Ozone

This study examines ozone (O₃) predictions from the Community Multiscale Air Quality (CMAQ) model version 4.5 and discusses potential factors influencing the model results. Daily maximum 8-h average O₃ levels are largely underpredicted when observed O₃ levels are above 85 ppb and overpredicted when they are below 35 ppb. Using a clustering approach, model performance was examined separately for several different synoptic regimes. Under the most common synoptic conditions of a typical summertime Bermuda High setup, the model showed good overall performance for O₃, while associations have been identified here between other, less frequent, synoptic regimes and the O₃ overprediction and underprediction biases. A sensitivity test between the CB-IV and CB05 chemical mechanisms showed that predictions of daily maximum 8-h average O₃ using CB05 were on average 7.3% higher than those using CB-IV. Boundary condition (BC) sensitivity tests show that the overprediction biases at low O₃ levels are more sensitive to the BC O₃ levels near the surface than BC concentrations aloft. These sensitivity tests also show the model performance for O₃ improved when using the global GEOS-CHEM BCs instead of default profiles. Simulations using the newest version of the CMAQ model (v4.6) showed a small improvement in O₃ predictions, particularly when vertical layers were not collapsed. Collectively, the results suggest that key synoptic weather patterns play a leading role in the prediction biases, and more detailed study of these episodes are needed to identify further modeling improvements.     

Modeling and optimization of wet flue gas desulfurization system based on a hybrid modeling method

Sulfur dioxide (SO₂) is one of the main air pollutants from many industries. Most coal-fired power plants in China use wet flue gas desulfurization (WFGD) as the main method for SO₂ removal. Presently, the operating of WFGD lacks accurate modeling method to predict outlet concentration, let alone optimization method. As a result, operating parameters and running status of WFGD are adjusted based on the experience of the experts, which brings about the possibility of material waste and excessive emissions. In this paper, a novel WFGD model combining a mathematical model and an artificial neural network (ANN) was developed to forecast SO₂ emissions. Operation data from a 1000-MW coal-fired unit was collected and divided into two separated sets for model training and validation. The hybrid model consisting a mechanism model and a 9-input ANN had the best performance on both training and validation sets in terms of RMSE (root mean square error) and MRE (mean relative error) and was chosen as the model used in optimization. A comprehensive cost model of WFGD was also constructed to estimate real-time operation cost. Based on the hybrid WFGD model and cost model, a particle swarm optimization (PSO)-based solver was designed to derive the cost-effective set points under different operation conditions. The optimization results demonstrated that the optimized operating parameters could effectively keep the SO₂ emissions within the standard, whereas the SO₂ emissions was decreased by 30.79% with less than 2% increase of total operating cost.

Implications: Sulfur dioxide (SO₂) is one of the main pollutants generated during coal combustion in power plants, and wet flue gas desulfurization (WFGD) is the main facility for SO₂ removal. A hybrid model combining SO₂ removal mathematical model with data-driven model achieves more accurate prediction of outlet concentration. Particle swarm optimization with a penalty function efficiently solves the optimization problem of WFGD subject to operation cost under multiple operation conditions. The proposed model and optimization method is able to direct the optimized operation of WFGD with enhanced emission and economic performance.     

Assimilative capacity–based emission load management in a critically polluted industrial cluster

In the present study, a modified approach was adopted to quantify the assimilative capacity (i.e., the maximum emission an area can take without violating the permissible pollutant standards) of a major industrial cluster (Manali, India) and to assess the effectiveness of adopted air pollution control measures at the region. Seasonal analysis of assimilative capacity was carried out corresponding to critical, high, medium, and low pollution levels to know the best and worst conditions for industrial operations. Bottom-up approach was employed to quantify sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (aerodynamic diameter <10 μm; PM₁₀) emissions at a fine spatial resolution of 500 × 500 m² in Manali industrial cluster. AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model), an U.S. Environmental Protection Agency (EPA) regulatory model, was used for estimating assimilative capacity. Results indicated that 22.8 tonnes/day of SO₂, 7.8 tonnes/day of NO₂, and 7.1 tonnes/day of PM₁₀ were emitted from the industries of Manali. The estimated assimilative capacities for SO₂, NO₂, and PM₁₀ were found to be 16.05, 17.36, and 19.78 tonnes/day, respectively. It was observed that the current SO₂ emissions were exceeding the estimated safe load by 6.7 tonnes/day, whereas PM₁₀ and NO₂ were within the safe limits. Seasonal analysis of assimilative capacity showed that post-monsoon had the lowest load-carrying capacity, followed by winter, summer, and monsoon seasons, and the allowable SO₂ emissions during post-monsoon and winter seasons were found to be 35% and 26% lower, respectively, when compared with monsoon season.

Implications: The authors present a modified approach for quantitative estimation of assimilative capacity of a critically polluted Indian industrial cluster. The authors developed a geo-coded fine-resolution PM₁₀, NO₂, and SO₂ emission inventory for Manali industrial area and further quantitatively estimated its season-wise assimilative capacities corresponding to various pollution levels. This quantitative representation of assimilative capacity (in terms of emissions), when compared with routine qualitative representation, provides better data for quantifying carrying capacity of an area. This information helps policy makers and regulatory authorities to develop an effective mitigation plan for air pollution abatement.     

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.     

Co-occurrence patterns of gaseous air pollutant pairs at different minimum concentrations in the United States

The frequency of co-occurrences for SO₂NO₂, SO₂/O₃ and O₃/NO₂ at rural and remote monitoring sites in the United States was characterized for the months of May-September for the years 1978–1982. Minimum hourly concentrations of 0.03 and 0.05 ppm of each gas were used as the criteria for defining a ‘co-occurrence’. The objectives of this study were to:

• 1.(1) identify the types of co-occurrence patterns and their frequency;
• 2.(2) identify whether the frequency of hourly simultaneous co-occurrences increased substantially when the minimum concentration was lowered (e.g. from 0.05 to 0.03 ppm) for each pollutant; and
• 3.(3) determine whether the frequency of co-occurrences showed large year-to-year variation.

For all pollutant pairs and co-occurrence thresholds (i.e. 0.03 and 0.05 ppm), the frequency of daily and hourly co-occurrences was low for most sites. Year-to-year variability was found to be insignificant; most of the monitoring sites experienced co-occurrences of any type less than 12% of the 153 days. Based on our observations, researchers attempting to assess the potential effects of SO₂/NO₂, SO₂/O₃ and O₃/NO₂ in the United States should construct simulated exposure regimes so that

• 1.(1) hourly simultaneous and daily simultaneous-only co-occurrences are fairly rare and
• 2.(2) when co-occurrences are present, complex-sequential and sequential-only co-occurrence patterns predominate.

     

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.     

Influence of sulfur dioxide-related interactions on PM2.5 formation in iron ore sintering

The formation of PM_2.5 (aerosol particulate matter less than 2.5 µm in aerodynamic diameter) in association with SO₂ emission during sintering process has been studied by dividing the whole sintering process into six typical sampling stages. A low-pressure cascade impactor was used to collect PM_2.5 by automatically segregating particulates into six sizes. It was found that strong correlation existed between the emission properties of PM_2.5 and SO₂. Wet mixture layer (overwetted layer and raw mixture layer) had the function to simultaneously capture SO₂ and PM_2.5 during the early sintering stages, and released them back into flue gas mainly in the flue gas temperature-rising period. CaSO₄ crystals constituted the main SO₂-related PM_2.5 during the disappearing process of overwetted layer, which was able to form perfect individual crystals or to form particles with complex chemical compositions. Besides the existence of individual CaSO₄ crystals, mixed crystals of K₂SO₄-CaSO₄ in PM_2.5 were also found during the first half of the temperature-rising period of flue gas. The interaction between fine-grained Ca-based fluxes, potassium vapors, and SO₂ was the potential source of SO₂-related PM_2.5.

Implications: The emission property of PM_2.5 and SO₂ throughout the sintering process exhibited well similarity. This phenomenon tightened the relationship between the formation of PM_2.5 and the emission of SO₂. Through revealing the properties of SO₂-related PM_2.5 during sintering process, the potential interaction between fine-grained Ca-based fluxes, potassium vapors, and SO₂ was found to be the source of SO₂-related PM_2.5. This information can serve as the guidance to develop efficient techniques to control the formation and emission of PM_2.5 in practical sintering plants.