Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

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 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

Which meteorological conditions produce worst-case odors from area sources?

Two competing meteorological factors influence atmospheric concentrations of pollutants from open liquid area sources such as wastewater treatment plant units: temperature and stability. High temperatures in summer produce greater emissions from liquid area sources because of increased compound volatility; however, these emissions tend to disperse more readily because of frequent occurrence of unstable conditions. An opposite scenario occurs in winter, with lesser emissions due to lower temperatures, but also frequently less dispersion, due to stable atmospheric conditions. The primary objective of this modeling study was thus to determine whether higher atmospheric concentrations from open liquid area sources occur more frequently in summer, when emissions are greater but so is dispersion, or in winter, when emissions are lesser but so is dispersion. The study utilized a rectangular clarifier emitting hydrogen sulfide as a sample open liquid area source. Dispersion modeling runs were conducted using ISCST3 and AERMOD, encompassing 5 yr of hourly meteorological data divided by season. Emission rates were varied hourly on the basis of a curve-fit developed from previously collected field data. Model output for each season was used to determine (1) maximum 2-min average concentrations, (2) the number of odor events (2-min average concentrations greater than odor detection thresholds), and (3) areas of impact. On the basis of these 3 types of output, it was found that the worst-case odors were associated with summer, considering impacts of meteorology upon both emissions and dispersion. Not accounting for the impact of meteorology on emissions (using a constant worst-case emission rate) caused concentrations to be overpredicted compared with a variable emission rate case. The highest concentrations occurred during stability classes D, E, and F, as anticipated. A comparison of ISCST3 and AERMOD found that for the area source modeled, ISCST3 predicted higher concentrations and more odor events for all seasons.     

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.     

The development of industry-specific odor impact criteria for feedlots using models

Emissions from feedlot operations are known to vary by environmental conditions and few if any techniques or models exist to predict the variability of odor emission rates from feedlots. The purpose of this paper is to outline and summarize unpublished reports that are the result of a collective effort to develop industry-specific odor impact criteria for Australian feedlots. This effort used over 250 olfactometry samples collected with a wind tunnel and past research to develop emission models for pads, sediment basins, holding ponds, and manure storage areas over a range of environmental conditions and tested using dynamic olfactometry. A process was developed to integrate these emission models into odor dispersion modeling for the development of impact criteria. The approach used a feedlot hydrology model to derive daily feedlot pad moisture, temperature, and thickness. A submodel converted these daily data to hourly data. A feedlot pad emissions model was developed that predicts feedlot pad emissions as a function of temperature, moisture content, and pad depth. Emissions from sediment basins and holding ponds were predicted using a basin emissions model as a function of days since rain, inflow volume, inflow ratio (pond volume), and temperature. This is the first attempt to model all odor source emissions from a feedlot as variable hourly emissions on the basis of climate, management, and site-specific conditions. Results from the holding pond, sediment basin, and manure storage emission models performed well, but additional work on the pad emissions model may be warranted. This methodology mimics the variable odor emissions and odor impact expected from feedlots due to climate and management effects. The main outcome of the work is the recognition that an industry-specific odor impact criterion must be expressed in terms of all of the components of the assessment methodology.     

Consultant Guide/1986

ABSTRACT

To obtain annual odor emission profiles from intensive swine operations, odor concentrations and emission rates were measured monthly from swine nursery, farrowing, and gestation rooms for a year. Large annual variations in odor concentrations and emissions were found in all the rooms and the impact of the seasonal factor (month) was significant (P < 0.05). Odor concentration was low in summer when ventilation rate was high but high in winter when ventilation rate was low, ranging from 362 (farrowing room in July) to 8934 (nursery room in December) olfactory unit (OU) m^?3. This indicates that the air quality regarding odor was significantly better in summer than that in winter. Odor emission rate did not show obvious seasonal pattern as odor concentration did, ranging from 2 (gestation room in November) to 90 (nursery room in April) OU m^?2 sec^?1; this explains why the odor complaints for swine barns have occurred all year round. The annual geometric mean odor concentration and emission rate of the nursery room was significantly higher than the other rooms (P < 0.05). In order to obtain the representative annual emission rate, measurements have to be taken at least monthly, and then the geometric mean of the monthly values will represent the annual emission rate. Incorporating odor control technologies in the nursery area will be the most efficient in reducing odor emission from the farm considering its emission rate was 2 to 3 times of the other areas. The swine grower-finisher area was the major odor source contributing 53% of odor emission of the farm and should also be targeted for odor control. Relatively positive correlations between odor concentration and both H₂S and CO₂ concentrations (R ² = 0.58) means that high level of these two gases might likely indicate high odor concentration in swine barns.

IMPLICATIONS The emissions of air pollutants including odors, greenhouse gases, and toxic gases have become a major environmental issue facing animal farms in the U.S.A. and Canada. To ensure the air quality in the vicinity of intensive livestock farms, air dispersion models have been used to determine setback distances between livestock facilities and neighboring residences based on certain air quality requirement on odor and gases. Due to the limited odor emission data available, none of the existing models can take account of seasonal variations of odor emissions, which may result in great uncertainties in setback distance calculations. Therefore, the obtained seasonal odor and gas emission rates by this study can be used by the government regulatory organizations and researchers in air dispersion modeling to get improved calculation of setback distances.     

Estimation of hydrogen sulfide emission rates at several wastewater treatment plants through experimental concentration measurements and dispersion modeling

The management and operation of wastewater treatment plants (WWTP) usually involve the release into the atmosphere of malodorous substances with the potential to reduce the quality of life of people living nearby. In this type of facility, anaerobic degradation processes contribute to the generation of hydrogen sulfide (H₂S), often at quite high concentrations; thus, the presence of this chemical compound in the atmosphere can be a good indicator of the occurrence and intensity of the olfactory impact in a specific area. The present paper describes the experimental and modelling work being carried out by CEAM-UMH in the surroundings of several wastewater treatment plants located in the Valencia Autonomous Community (Spain). This work has permitted the estimation of H₂S emission rates at different WWTPs under different environmental and operating conditions. Our methodological approach for analyzing and describing the most relevant aspects of the olfactory impact consisted of several experimental campaigns involving intensive field measurements using passive samplers in the vicinity of several WWTPs, in combination with numerical simulation results from a diagnostic dispersion model. A meteorological tower at each WWTP provided the input values for the dispersion code, ensuring a good fit of the advective component and therefore more confidence in the modelled concentration field in response to environmental conditions. Then, comparisons between simulated and experimental H₂S concentrations yielded estimates of the global emission rate for this substance at several WWTPs at different time periods. The results obtained show a certain degree of temporal and spatial (between-plant) variability (possibly due to both operational and environmental conditions). Nevertheless, and more importantly, the results show a high degree of uniformity in the estimates, which consistently stay within the same order of magnitude.

Implications: Estimating emissions to the atmosphere is usually considered a complex task, especially when such discharge comes from diffuse or uncontrolled sources. In any approach to air quality control, just from the point of view of increasing knowledge or as a management problem in order to reduce present levels of pollution, accurate estimation of emission rates is revealed as a fundamental step. Evaluation from an indirect method provides a useful methodology in such cases. Combination of dispersion modeling with experimental air concentration measurements permits one to obtain a first estimation of H₂S emission rates at several wastewater treatment plants. In a subsequent refinement of the process, the initial constant average emissions calculated were improved, leading to the formulation of a time-varying emission model, as a function of environmental quantities.     

Application of a Combined Measurement and Modeling Method to Quantify Windblown Dust Emissions from the Exposed Playa at Mono Lake,California

ABSTRACT

Particulate matter ≤10 μm (PM₁₀) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM₁₀ monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km² area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM₁₀ concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM₁₀ concentrations to back-calculate the ratio of vertical PM₁₀ flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K _f) were found to change seasonally, ranging from 1.3 × 10^?5 to 5.1 × 10^?5 for sand flux measured at 15 cm above the surface (q ₁₅). Hourly PM₁₀ emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F?=?K _f ×?q ₁₅). The maximum hourly PM10 emission rate from the study area was 76 g/m²·hr (10-m wind speed?=?23.5 m/sec). Maximum daily PM₁₀ emissions were estimated at 450 g/m²·day, and annual emissions at 1095 g/m²·yr. Hourly PM₁₀ emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM₁₀ ranging from 16 to over 60,000 μg/m³ (slope?=?0.89, R ²?=?0.77).

IMPLICATIONS Under a U.S. Environmental Protection Agency (EPA)-approved plan, the method described in this paper has been used since 2000 at Owens Lake, CA, to identify and successfully mitigate dust from over 100 km² of the dry lakebed. It continues to be used to monitor dust control compliance at Owens Lake. Scaled-down versions of the Owens Lake network can be implemented in other areas in a manner similar to the Mono Lake study. Once K-factors are established, low-cost CSC samplers (about $35 U.S.) may be used for periodic monitoring (e.g., daily, weekly, or monthly) to estimate PM₁₀ emissions or to evaluate dust control compliance.     

An Inversion Algorithm for Determining Area-Source Emissions from Downwind Concentration Measurements

Measuring emissions from nonuniform area sources, such as waste repository sites, has been a difficult problem. A simple but reliable method is not available. An objective method of inverting downwind concentration measurements, utilizing an assumed form of atmospheric dispersion to reconstruct total emission rate and distribution, is described in this study. The Gaussian dispersion model is compared to a more realistic model based on K-theory and similarity expressions. A sensitivity analysis is presented indicating the atmospheric conditions under which a successful application of the method could be anticipated. Field releases of sulfur hexaf luoride (SF₆) from a simulated area source in flat terrain were conducted to check the method,ability to reconstruct source distribution, and total emission rate. The sensitivity analysis and the field study confirm that a few ground-level concentration measurements and a simple determination of the atmospheric dispersion characteristics are sufficient, under neutral to stable conditions, to obtain the total emission rate accurately. Reconstruction of the spatial pattern of the source is possible by utilizing concentration information from samplers located on two separate ground-level receptor lines, if a shift in the wind direction occurs and if it can be assumed that the total emission rate is time invariant. A method of cross-checking the accuracy of the reconstruction, using a simultaneous tracer release, is presented.     

Near-Field Dispersion Modeling for Regulatory Applications

Abstract

This paper evaluates the application of dispersion models to estimate near-field pollutant concentrations in two case studies. The Industrial Source Complex Short-Term Model (ISCST3) was evaluated with hexavalent chromium measurements collected within 100 m of two facilities in Barrio Logan, San Diego, CA. ISCST3 provided reasonable estimates for higher pollutant concentrations but underestimated lower concentrations. To understand the observed distribution of concentrations in Barrio Logan, a recently conducted tracer experiment was analyzed. The tracer, sulfur hexafluoride, was released at ambient temperature from an urban facility at the University of California at Riverside, and concentrations were measured within 20 m of the source. Modeling results indicated that Industrial Source Complex–Plume Rise Model Enhancement and American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model–Plume Rise Model Enhancement overestimated high concentrations and underestimated low concentrations. A diagnostic study with a simple Gaussian dispersion model that incorporated site-specific meteorology was used to evaluate model results. This study found that incorporating lateral meandering for nonbuoyant urban plumes in Gaussian dispersion models could improve concentration estimates even when downwash is not considered. Incorporating a meandering component in ISCST3 resulted in improvements in estimating hexavalent chromium concentrations in Barrio Logan. Credible near-source concentration estimates depend on accurate characterization of emissions, onsite micrometeorology, and a method to account for lateral meandering in the near field.     

Preventive control of odor emissions through manipulation of operational parameters during the active phase of composting

Better understanding of the effects of key operational parameters or environmental factors on odor emission is of critical importance for minimizing the generation of composting odors. A series of laboratory experiments was conducted to examine the effects of various operating conditions on odor emissions. The results revealed that airflow rates that were too high or too low could result in higher total odor emissions. An optimal flowrate for odor control would be approximately 0.6 L/min.kg dry matter with intermittent aeration and a duty cycle of 33%. Temperature setpoint at 60°C appeared to be a turning point for odor emission. Below this point, odor emissions increased with increasing temperature setpoint; conversely, odor emissions decreased with increasing temperature setpoint above this point. With regard to the composting material properties, odor emissions were greatly affected by the initial moisture content of feedstock. Both peak odor concentration and emission rate generally increased with higher initial moisture content. Odor emission was significant only at moisture levels higher than 65%. An initial moisture level below 45% is not recommended due to concern with the resulting lower degree of biodegradation. Biodegradable volatile solids content (BVS) of feedstock had pronounced effect on odor emissions. Peak odor concentration and emission rate increased dramatically as BVS increased from 45% to 65%, thus, total odor emission increased exponentially with BVS.     

Quantification of Hourly Variability in NOx Emissions for Baseload Coal-Fired Power Plants

Abstract

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 method to estimate emission rates from industrial stacks based on neural networks

This paper presents a technique based on artificial neural networks (ANN) to estimate pollutant rates of emission from industrial stacks, on the basis of pollutant concentrations measured on the ground. The ANN is trained on data generated by the ISCST3 model, widely accepted for evaluation of dispersion of primary pollutants as a part of an environmental impact study. Simulations using theoretical values and comparison with field data are done, obtaining good results in both cases at predicting emission rates. The application of this technique would allow the local environment authority to control emissions from industrial plants without need of performing direct measurements inside the plant.     

Near-field dispersion modeling for regulatory applications

This paper evaluates the application of dispersion models to estimate near-field pollutant concentrations in two case studies. The Industrial Source Complex Short-Term Model (ISCST3) was evaluated with hexavalent chromium measurements collected within 100 m of two facilities in Barrio Logan, San Diego, CA. ISCST3 provided reasonable estimates for higher pollutant concentrations but underestimated lower concentrations. To understand the observed distribution of concentrations in Barrio Logan, a recently conducted tracer experiment was analyzed. The tracer, sulfur hexafluoride, was released at ambient temperature from an urban facility at the University of California at Riverside, and concentrations were measured within 20 m of the source. Modeling results indicated that Industrial Source Complex-Plume Rise Model Enhancement and American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model-Plume Rise Model Enhancement overestimated high concentrations and underestimated low concentrations. A diagnostic study with a simple Gaussian dispersion model that incorporated site-specific meteorology was used to evaluate model results. This study found that incorporating lateral meandering for nonbuoyant urban plumes in Gaussian dispersion models could improve concentration estimates even when downwash is not considered. Incorporating a meandering component in ISCST3 resulted in improvements in estimating hexavalent chromium concentrations in Barrio Logan. Credible near-source concentration estimates depend on accurate characterization of emissions, onsite micrometeorology, and a method to account for lateral meandering in the near field.     

Application of a combined measurement and modeling method to quantify windblown dust emissions from the exposed playa at Mono Lake, California

Particulate matter < or =10 microm (PM10) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM10 monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km2 area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM10 concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM10 concentrations to back-calculate the ratio of vertical PM10 flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K(f)) were found to change seasonally, ranging from 1.3 x 10(-5) to 5.1 x 10(-5) for sand flux measured at 15 cm above the surface (q15). Hourly PM10 emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F = K(f) x q15). The maximum hourly PM10 emission rate from the study area was 76 g/m2 x hr (10-m wind speed = 23.5 m/sec). Maximum daily PM10 emissions were estimated at 450 g/m2 x day, and annual emissions at 1095 g/m2 x yr. Hourly PM10 emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM10 ranging from 16 to over 60,000 microg/m3 (slope = 0.89, R2 = 0.77).     

PM Emissions Emanating from Limited-Access Highways

ABSTRACT

Motor vehicle contributions to primary particulate matter (PM) emissions include exhaust, tire wear, brake and clutch wear, and resuspended road dust. Relatively few field studies have been conducted to quantify fleetaverage exhaust emissions for actual on-road conditions. Therefore, direct measurements of motor vehicle-related PM emissions are warranted. In this study, PM₁₀ and PM_2.5 mass concentrations were measured near two major highways in the St. Louis area over the period from February–April 1997. Samplers were deployed both upwind and downwind of the roadways to capture the transport and dispersion of PM with distance from the roadway. The observed microscale concentration fields were compared to estimates using the PART5 emission factor model together with the CALINE4 highway dispersion model. Traffic- induced PM mass concentrations observed downwind of the roadway were always less than PART5/CALINE4 predictions; average percent differences for observed traffic-induced mass concentrations compared to predicted values were ?34% for PM_2.5 and -70% for PM₁₀. In most cases, the observed PM concentration decay with increasing distance from the roadway was steeper than predicted by dispersion modeling. Motor vehicle-induced emission factors were reconstructed by fitting CALINE4 to the observed concentration data with the emission factor as the sole adjustable parameter. Reconstructed fleet-average motor vehicle emission factors for the urban interstate highway were 0.03–0.04 g/VMT for both PM_2.5 and PM₁₀, while the fleet-average emission factors for the rural interstate highway were 0.2 and 0.3 g/VMT for PM_2.5 and PM₁₀, respectively.     

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.     

Estimating Gas Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

Abstract

Manure storage tanks and animals in barns are important agricultural sources of methane. To examine the possibility of using an inverse dispersion technique based on a backward Lagrangian Stochastic (bLS) model to quantify methane (CH₄) emissions from multiple on-farm sources, a series of tests were carried out with four possible source configurations and three controlled area sources. The simulated configurations were: (C1) three spatially separate ground-level sources, (C2) three spatially separate sources with wind-flow disturbance, (C3) three adjacent ground-level sources to simulate a group of adjacent sources with different emission rates, and (C4) a configuration with a ground level and two elevated sources. For multiple ground-level sources without flow obstructions (C1 and C3), we can use the condition number (k, the ratio of the uncertainty in the calculated emission rate to the uncertainty in the predicted ratio of concentration to emission rate) to evaluate the applicability of this inverse dispersion technique and a preliminary threshold of k < 10 is recommended. For multiple sources with wind disturbance (C2) or an even more complex configuration including ground level and elevated sources (C4), a low k is not sufficient to provide reasonable discrete and total emission rates. The effect of flow obstructions can be neglected as long as the distance between the source and the measurement location is greater than approximately 10 times the height of the flow obstructions. This study shows that the bLS model has the potential to provide accurate discrete emission rates from multiple on-farm emissions of gases provided that certain conditions are met.     

Annual variations of odor concentrations and emissions from swine gestation, farrowing, and nursery buildings

To obtain annual odor emission profiles from intensive swine operations, odor concentrations and emission rates were measured monthly from swine nursery, farrowing, and gestation rooms for a year. Large annual variations in odor concentrations and emissions were found in all the rooms and the impact of the seasonal factor (month) was significant (P < 0.05). Odor concentration was low in summer when ventilation rate was high but high in winter when ventilation rate was low, ranging from 362 (farrowing room in July) to 8934 (nursery room in December) olfactory unit (OU) m(-3). This indicates that the air quality regarding odor was significantly better in summer than that in winter. Odor emission rate did not show obvious seasonal pattern as odor concentration did, ranging from 2 (gestation room in November) to 90 (nursery room in April) OU m(-2) sec(-1); this explains why the odor complaints for swine barns have occurred all year round. The annual geometric mean odor concentration and emission rate of the nursery room was significantly higher than the other rooms (P < 0.05). In order to obtain the representative annual emission rate, measurements have to be taken at least monthly, and then the geometric mean of the monthly values will represent the annual emission rate. Incorporating odor control technologies in the nursery area will be the most efficient in reducing odor emission from the farm considering its emission rate was 2 to 3 times of the other areas. The swine grower-finisher area was the major odor source contributing 53% of odor emission of the farm and should also be targeted for odor control. Relatively positive correlations between odor concentration and both H2S and CO2 concentrations (R(2) = 0.58) means that high level of these two gases might likely indicate high odor concentration in swine barns.     

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.     

Models to Estimate Volatile Organic Hazardous Air Pollutant Emissions from Municipal Sewer Systems

Abstract

Emissions from municipal sewers are usually omitted from hazardous air pollutant (HAP) emission inventories. This omission may result from a lack of appreciation for the potential emission impact and/or from inadequate emission estimation procedures. This paper presents an analysis and comparison of the models available to estimate volatile organic HAP (VOHAP) emissions from sewers. Comparisons were made between the different theoretical foundations of the models, as well as between the emissions predicted by the models for a single sewer component. Sewer gas concentrations predicted by the models were also compared to measured sewer gas concentrations reported in the literature. Two of the models were compared in their ability to estimate sewer VOHAP emissions for a large U. S. city using National Pollution Discharge Effluent System data for the influent wastewater to the city's municipal wastewater treatment facilities. This estimate showed that, regardless of the model used, sewer emissions are a potentially significant source of VOHAP emissions in the urban environment. The choice of model, however, is thought to be less critical to sewer emission estimates than the source of sewer wastewater VOHAP concentration data.