Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty

The intent of this paper is to relate the magnitude of the error bounds of data, used as inputs to a Gaussian dispersion model, to the magnitude of the error bounds of the model output, which include the estimates of the maximum concentration and the distance to that maximum. The research specifically addresses the uncertainty in estimating the maximum concentrations from elevated buoyant sources during unstable atmospheric conditions, as these are most often of practical concern in regulatory decision making. A direct and quantitative link between the nature and magnitude of the input uncertainty and modeling results has not been previously investigated extensively. The ability to develop specific error bounds, tailored to the modeling situation, allows more informed application of the model estimates to the air quality issues.In this study, a numerical uncertainty analysis is performed using the Monte-Carlo technique to propagate the uncertainties associated with the model input. Uncertainties were assumed to exist in four model input parameters: (1) wind speed, (2) standard deviation of lateral wind direction fluctuations, (3) standard deviation of vertical wind direction fluctuations, and (4) plume rise. For each simulation, results were summarized characterizing the uncertainty in four features of the ground-level concentration pattern predicted by the model: (1) the magnitude of the maximum concentration, (2) the distance to the maximum concentration, and (3) and (4) the areas enclosed within the isopleths of 50% and 25% of the error-free estimate of maximum concentration.The authors conclude that the error bounds for the estimated maximum concentration and the distance to the maximum can be double that of the error bounds for individual model input parameters. The model output error bounds for the areas enclosed within isopleth values can be triple the error bounds of the input. It was not our intent to cover all possible combinations for the error in the input parameters. Ours was a much more limited goal of providing a lower bound estimate of model uncertainty in which we assume the input is reasonably well characterized and there is no bias in the input. These results allow estimation of minimum bounds on errors in model output when considering reasonable input error bounds.     

A sensitivity study of separation distances calculated with the Austrian Odour Dispersion Model (AODM)

The Austrian Odour Dispersion Model (AODM) is a Gaussian model adapted for the prediction of odour sensation. It estimates the daily and seasonal variation of the odour emission, the average, ambient odour concentration and the momentary (peak) concentration for the time-interval of a single human breath (approx. 5 s). Peak concentrations, further downwind, are modified by use of an exponential attenuation function for which the ratios of the standard deviations of the wind components to the average wind speed have either to be taken from the literature or to be calculated, e.g. from ultrasonic anemometer data.AODM calculates direction-dependent separation distances for a combination of odour threshold and exceedence probability, which are a function of the prevailing wind velocity and atmospheric stability conditions. Meteorological time series from one site in Styria in southern Austria and one site in the Austrian flatlands, North of the Alps, both rural, are used for a sensitivity study of separation distances. One aspect is, how two different schemes to determine atmospheric stability influence the separation distances. Another source of uncertainty of the calculated separation distances results from the use of measured or literature values for the ratios mentioned above. Decisions on which schemes or ratios to be used have a decisive influence on the separation distances.     

A framework for error analysis of a long-range transport model with emphasis on parameter uncertainty

A comprehensive framework for model error analysis is applied to the EMEP-W model of longrange transport of sulfur in Europe. This framework includes a proposed taxonomy of model uncertainties. Parameter uncertainties were investigated by Monte Carlo simulation of two source-receptor combinations. A 20% input parameter uncertainty (expressed as a coefficient of variation = standard deviation/mean) yielded a 15–22% output error of total sulfur deposition. The relationship between output error and input uncertainty was approximately proportional. Covariance between parameters can have an important effect on computed model error, and can either exaggerate or reduce errors compared to the uncorrelated case. Of the model state variables, SO₂ air concentration and wet deposition had the highest error, and total sulfur deposition the lowest. It was also found that it is more important to specify the dispersion of the input parameter frequency distributions than their shape. The results of the model error analysis were applied to routine calculations of deposition in Europe. An error (coefficient of variation) of 20% for transfer coefficients throughout Europe yielded spatial variations in the order of a few tens to a few hundreds of km in computed deposition isolines of 2 and 5 g sulfur m⁻²a⁻¹.     

PM2.5 speciation trends network: evaluation of whole-system uncertainties using data from sites with collocated samplers

The objectives of this paper are to contrast the relative variability of replicate laboratory measurements of selected chemical components of fine particulate matter (PM) with total variability from collocated measurements and to compare the magnitudes of the uncertainties determined from collocated sampler data with those currently being provided to U.S. Environmental Protection Agency (EPA)'s Air Quality System (AQS) database by RTI International (RTI). Pointwise uncertainty values are needed for modeling and data analysis and should include all the random errors affecting each data point. Total uncertainty can be decomposed into two primary components: analytical measurement uncertainty and sampling uncertainty. Analytical measurement uncertainties are relatively easy to calculate from routine quality control (QC) data. Sampling uncertainties, on the other hand, are comparatively difficult to measure. In this paper, the authors describe data from collocated samplers to provide a snapshot of whole-system uncertainty for several important chemical species. The components of uncertainty were evaluated for key species from each of the analytical methods employed by the PM2.5 Speciation Trends Network (STN) program: gravimetry, ion chromatography (IC), X-ray fluorescence (XRF), and thermal-optical analysis for organic carbon and elemental carbon. The results show that the laboratory measurement uncertainties are typically very small compared with uncertainties calculated from the differences between samples collected from collocated samplers. These differences are attributable to the "field" components uncertainty, which may include contamination and/or losses during shipping, handling, and sampling, as well as other distortions of the concentration level due to flow and sample volume variations. Uncertainties calculated from the collocation results were found to be generally similar to the uncertainties currently being loaded into EPA's AQS system, with some exceptions described below.     

Assessing the uncertainty associated with national greenhouse gas emission inventories:: a case study for Austria

The uncertainty associated with the Austrian Greenhouse Gas emission inventory has been determined for the gases CO₂, CH₄ and N₂O and for the overall greenhouse potential. Expert interviews were conducted to obtain uncertainties in inventory input data. Based on these interviews, error distributions were developed and combined using Monte-Carlo analysis. Results for all sources and gases combined indicate an overall uncertainty between 10.5% and 12% depending on the base year considered. Excluding emissions and the uncertainty associated with forest sinks and natural sources, overall uncertainty decreased by 2% points. The mere ‘random error’, which is considered the level of uncertainty to be achieved with the current methodology (excluding all systematic errors) is 5% points lower. Detailed evaluation shows that much of the overall uncertainty derives from a lack of understanding the processes associated with N₂O emissions from soils. Other important contributors to GHG emission uncertainties are CH₄ from landfills and forests as CO₂ sinks. The uncertainty of the trend has been determined at near 5% points, with solid waste production (landfills) having the strongest contribution. Theoretical considerations do not permit a decrease of the trend uncertainty—even when forest sinks are not considered—below 3% points.     

Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results

The chemical mass balance (CMB) model was applied for source apportionment of PM_2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.     

Development of probabilistic emission inventories of air toxics for Jacksonville, Florida, USA

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as -25 to +30% for Hg to as large as -83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.     

Design and Verification of a Programmable Gas Dispensing System for Exposing Plants to Dynamic Concentrations of Air Pollutants

Abstract

Landfills represent a source of distributed emissions source over an irregular and heterogeneous surface. In the method termed “Other Test Method-10” (OTM-10), the U.S. Environmental Protection Agency (EPA) has proposed a method to quantify emissions from such sources by the use of vertical radial plume mapping (VRPM) techniques combined with measurement of wind speed to determine the average emission flux per unit area per time from nonpoint sources. In such application, the VRPM is used as a tool to estimate the mass of the gas of interest crossing a vertical plane. This estimation is done by fitting the field-measured concentration spatial data to a Gaussian or some other distribution to define a plume crossing the vertical plane. When this technique is applied to landfill surfaces, the VRPM plane may be within the emitting source area itself. The objective of this study was to investigate uncertainties associated with using OTM-10 for landfills. The spatial variability of emission in the emitting domain can lead to uncertainties of –34 to 190% in the measured flux value when idealistic scenarios were simulated. The level of uncertainty might be higher when the number and locations of emitting sources are not known (typical field conditions). The level of uncertainty can be reduced by improving the layout of the VRPM plane in the field in accordance with an initial survey of the emission patterns. The change in wind direction during an OTM-10 testing setup can introduce an uncertainty of 20% of the measured flux value. This study also provides estimates of the area contributing to flux (ACF) to be used in conjunction with OTM-10 procedures. The estimate of ACF is a function of the atmospheric stability class and has an uncertainty of 10–30%.     

Air pollution modeling at road sides using the operational street pollution model--a case study in Hanoi, Vietnam

In many metropolitan areas, traffic is the main source of air pollution. The high concentrations of pollutants in streets have the potential to affect human health. Therefore, estimation of air pollution at the street level is required for health impact assessment. This task has been carried out in many developed countries by a combination of air quality measurements and modeling. This study focuses on how to apply a dispersion model to cities in the developing world, where model input data and data from air quality monitoring stations are limited or of varying quality. This research uses the operational street pollution model (OSPM) developed by the National Environmental Research Institute in Denmark for a case study in Hanoi, the capital of Vietnam. OSPM predictions from five streets were evaluated against air pollution measurements of nitrogen oxides (NO(x)), sulfur dioxide (SO2), carbon monoxide (CO), and benzene (BNZ) that were available from previous studies. Hourly measurements and passive sample measurements collected over 3-week periods were compared with model outputs, applying emission factors from previous studies. In addition, so-called "backward calculations" were performed to adapt the emission factors for Hanoi conditions. The average fleet emission factors estimated can be used for emission calculations at other streets in Hanoi and in other locations in Southeast Asia with similar vehicle types. This study also emphasizes the need to further eliminate uncertainties in input data for the street-scale air pollution modeling in Vietnam, namely by providing reliable emission factors and hourly air pollution measurements of high quality.     

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%).     

Assignment of emission abatement levels by stochastic programming

The paper deals with the problem of determining the optimal emission abatement policy in a region. The criterion used consists of looking for the policy which minimizes the overall abatement cost under the constraint of meeting a given ambient standard. This policy is determined by a formal optimization model (mathematical program). Pollutant dispersion is affected by uncertainty, related to the meteorological situation in the region, thus the satisfaction of the ambient standard constraint in the program can be regarded as a random event (‘stochastic programming approach’). Two solutions for the stochastic program are suggested: the distribution approach (which leads to the definition of Pareto alternatives between abatement cost and risk of violating the standard) and chance-constraints programming (the satisfaction of the standard is required with a given probability, at least).     

Development of Probabilistic Emission Inventories of Air Toxics for Jacksonville,Florida

Abstract

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as –25 to +30% for Hg to as large as –83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.     

Estimating and comparing greenhouse gas emissions with their uncertainties using different methods: A case study for an energy supply utility

Energy supply utilities release significant amounts of greenhouse gases (GHGs) into the atmosphere. It is essential to accurately estimate GHG emissions with their uncertainties, for reducing GHG emissions and mitigating climate change. GHG emissions can be calculated by an activity-based method (i.e., fuel consumption) and continuous emission measurement (CEM). In this study, GHG emissions such as CO₂, CH₄, and N₂O are estimated for a heat generation utility, which uses bituminous coal as fuel, by applying both the activity-based method and CEM. CO₂ emissions by the activity-based method are 12–19% less than that by the CEM, while N₂O and CH₄ emissions by the activity-based method are two orders of magnitude and 60% less than those by the CEM, respectively. Comparing GHG emissions (as CO₂ equivalent) from both methods, total GHG emissions by the activity-based methods are 12–27% lower than that by the CEM, as CO₂ and N₂O emissions are lower than those by the CEM. Results from uncertainty estimation show that uncertainties in the GHG emissions by the activity-based methods range from 3.4% to about 20%, from 67% to 900%, and from about 70% to about 200% for CO₂, N₂O, and CH₄, respectively, while uncertainties in the GHG emissions by the CEM range from 4% to 4.5%. For the activity-based methods, an uncertainty in the Intergovernmental Panel on Climate Change (IPCC) default net calorific value (NCV) is the major uncertainty contributor to CO₂ emissions, while an uncertainty in the IPCC default emission factor is the major uncertainty contributor to CH₄ and N₂O emissions. For the CEM, an uncertainty in volumetric flow measurement, especially for the distribution of the volumetric flow rate in a stack, is the major uncertainty contributor to all GHG emissions, while uncertainties in concentration measurements contribute a little to uncertainties in the GHG emissions.

Implications:Energy supply utilities contribute a significant portion of the global greenhouse gas (GHG) emissions. It is important to accurately estimate GHG emissions with their uncertainties for reducing GHG emissions and mitigating climate change. GHG emissions can be estimated by an activity-based method and by continuous emission measurement (CEM), yet little study has been done to calculate GHG emissions with uncertainty analysis. This study estimates GHG emissions and their uncertainties, and also identifies major uncertainty contributors for each method.     

Screening Models for Estimating Toxic Air Pollution Near a Hazardous Waste Landfill

This paper is directed to environmental scientists concerned with assessing toxic air pollution downwind of hazardous waste landfills to determine whether potential health threats or exceedances of air quality standards exist. The purpose of this paper is to evaluate the performance of four air quality screening models.

The emission rate of vinyl chloride from the BKK co-disposal landfill in West Covina, California is estimated. Ambient vinyl chloride concentrations are estimated using a ground level point source model, two virtual point source models, arid the simple box model with meteorological and landfill input data representative of periods when ambient monitoring was conducted. The two virtual point source models are most precise and accurate in estimating 24-hour vinyl chloride concentrations. However, the results could include compensating errors in the emission rate and dispersion calculations because the emission rate estimate could not be independently evaluated.     

Priority assessment of toxic substances in life cycle assessment. Part II: assessing parameter uncertainty and human variability in the calculation of toxicity potentials

Toxicity potentials are standard values used in life cycle assessment (LCA) to enable a comparison of toxic impacts between substances. This paper presents the results of an uncertainty assessment of toxicity potentials that were calculated with the global nested multi-media fate, exposure and effects model USES-LCA. The variance in toxicity potentials resulting from input parameter uncertainties and human variability was quantified by means of Monte Carlo analysis with Latin Hypercube sampling (LHS). For Atrazine, 2,3,7,8-TCDD and Lead, variation, expressed by the ratio of the 97.5%-ile and the 2.5%-ile, ranges from about 1.5 to 6 orders of magnitude. The major part of this variation originates from a limited set of substance-specific input parameters, i.e. parameters that describe transport mechanisms, substance degradation, indirect exposure routes and no-effect concentrations. Considerable correlations were found between the toxicity potentials of one substance, in particular within one impact category. The uncertainties and correlations reported in the present study may have a significant impact on the outcome of LCA case studies.     

A simple model for the dispersion of pollutants from a road tunnel portal

The dispersion of pollutants from a roadway tunnel portal is mainly determined by the interaction between the ambient wind and the jet stream from the tunnel portal. In principal, Eulerian microscale models by solving the conservation equations for mass, momentum, and energy, are thus able to simulate effects such as buoyancy etc. properly. However, for engineering applications such models need too much CPU time, and are not easy to handle by non-scientific personnel. Only a few dispersion models, applicable for regulatory purposes, have so far appeared in the literature. These models are either empirical models not always applicable for different sites, or they do not capture important physical effects like buoyancy phenomena. Here, a rather simple model is presented, which takes into account most of the important processes considered to govern the dispersion of a jet stream from portals. These are the exit velocity, the buoyancy, the influence of ambient wind direction fluctuations on the position of the jet stream, and traffic induced turbulence. Although the model contains some heuristic elements, it was successfully tested against tracer experiments taken near a motorway tunnel portal in Austria. The model requires relatively little CPU time. Current limitations of the model include the neglect of terrain, building, and vehicle effects on the dispersion, and the neglect of the horizontal dispersion arising from entrainment of ambient air in the jet stream. The latter could lead to an underestimation of plume spreads for higher wind speeds. The validation of the model will be the focus of future research. The experimental data set is also available for the scientific community.     

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%).     

Evaluation of the measurement uncertainty in automated long-term sampling of PCDD/PCDFs

Since the publication of the first version of European standard EN-1948 in 1996, long-term sampling equipment has been improved to a high standard for the sampling and analysis of polychlorodibenzo-p-dioxin (PCDD)/polychlorodibenzofuran (PCDF) emissions from industrial sources. The current automated PCDD/PCDF sampling systems enable to extend the measurement time from 6–8 h to 15–30 days in order to have data values better representative of the real pollutant emission of the plant in the long period. EN-1948:2006 is still the European technical reference standard for the determination of PCDD/PCDF from stationary source emissions. In this paper, a methodology to estimate the measurement uncertainty of long-term automated sampling is presented. The methodology has been tested on a set of high concentration sampling data resulting from a specific experience; it is proposed with the intent that it is to be applied on further similar studies and generalized. A comparison between short-term sampling data resulting from manual and automated parallel measurements has been considered also in order to verify the feasibility and usefulness of automated systems and to establish correlations between results of the two methods to use a manual method for calibration of automatic long-term one. The uncertainty components of the manual method are analyzed, following the requirements of EN-1948-3:2006, allowing to have a preliminary evaluation of the corresponding uncertainty components of the automated system. Then, a comparison between experimental data coming from parallel sampling campaigns carried out in short- and long-term sampling periods is realized. Long-term sampling is more reliable to monitor PCDD/PCDF emissions than occasional short-term sampling. Automated sampling systems can assure very useful emission data both in short and long sampling periods. Despite this, due to the different application of the long-term sampling systems, the automated results could not be directly compared with manual results, not even in terms of measurement uncertainty. This investigation focuses on both uncertainty and repeatability of the automated sampling method. The standard 20988, developed by Internarional Organization of Standardization (ISO) can be used to estimate the measurement uncertainty. The results confirm that the uncertainties of manual and automated methods are comparable. At the same time, it is not appropriate to consider the manual method as a reference for the evaluation of the uncertainty of the automated sampling system, due to the high variability of both systems.