Uncertainties in Air Toxics Calculated by the Dispersion Models AERMOD and ISCST3 in the Houston Ship Channel Area

Abstract The uncertainties in simulations of annually averaged concentrations of two air toxics (benzene and 1,3-butadiene) are estimated for two widely used U.S. air quality models, the Industrial Source Complex Short-Term, version 3, (ISCST3) model and the American Meteorological Society–Environmental Protection Agency Model (AERMOD). The effects of uncertainties in emissions input, meteorological input, and dispersion model parameters are investigated using Monte Carlo probabilistic uncertainty methods, which involve simultaneous random and independent perturbations of all inputs. The focus is on a 15 km × 15 km domain in the Houston, Texas, ship channel area. Concentrations are calculated at hypothetical receptors located at the centroids of population census tracts. The model outputs that are analyzed are the maximum annually averaged maximum concentration at any single census tract or monitor as well as the annually averaged concentration averaged over the census tracts. The input emissions uncertainties are estimated to be about a factor of 3 (i.e., covering the 95% range) for each of several major categories. The uncertainties in meteorological inputs (such as wind speed) and dispersion model parameters (such as the vertical dispersion coefficient σz) also are estimated. The results show that the 95% range in predicted annually averaged concentrations is about a factor of 2–3 for the air toxics, with little variation by model. The input variables whose variations have the strongest effect on the predicted concentrations are on-road mobile sources and some industrial sources (dependent on chemical), as well as wind speed, surface roughness, and σz. In most scenarios, the uncertainties of the emissions input group contribute more to the total uncertainty than do the uncertainties of the meteorological/dispersion input group.

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AERMOD: A Dispersion Model for Industrial Source Applications. Part II: Model Performance against 17 Field Study Databases

Journal of Applied Meteorology ◽

10.1175/jam2228.1 ◽

2005 ◽

Vol 44 (5) ◽

pp. 694-708 ◽

Cited By ~ 114

Author(s):

Steven G. Perry ◽

Alan J. Cimorelli ◽

Robert J. Paine ◽

Roger W. Brode ◽

Jeffrey C. Weil ◽

...

Keyword(s):

Field Study ◽

Environmental Protection Agency ◽

Dispersion Model ◽

Model Performance ◽

Field Measurements ◽

American Meteorological Society ◽

Air Dispersion ◽

Wake Effects ◽

Air Dispersion Model ◽

Regulatory Model

Abstract The performance of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model against 17 field study databases is described. AERMOD is a steady-state plume model with significant improvements over commonly applied regulatory models. The databases are characterized, and the performance measures are described. Emphasis is placed on statistics that demonstrate the model’s abilities to reproduce the upper end of the concentration distribution. This is most important for applied regulatory modeling. The field measurements are characterized by flat and complex terrain, urban and rural conditions, and elevated and surface releases with and without building wake effects. As is indicated by comparisons of modeled and observed concentration distributions, with few exceptions AERMOD’s performance is superior to that of the other applied models tested. This is the second of two articles, with the first describing the model formulations.

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Dispersion Effects of Particulate Lead (Pb) from the Stack of a Lead Battery Recycling Plant

Energies ◽

10.3390/en13215690 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5690

Author(s):

Dimitra Karali ◽

Alexandros Stavridis ◽

Glykeria Loupa ◽

Spyridon Rapsomanikis

Keyword(s):

Environmental Protection Agency ◽

Dispersion Model ◽

Industrial Area ◽

Operating Conditions ◽

Total Suspended Particulate ◽

Lead Battery ◽

American Meteorological Society ◽

Battery Recycling ◽

Suspended Particulate ◽

Online Measurements

The contribution of emissions from the stack of a lead battery recycling plant to atmospheric lead concentrations and, eventually, to the topsoil of the surrounding area, were studied. A Gaussian dispersion model, of the American Meteorological Society/United States Environmental Protection Agency Regulatory Model, (AERMOD) was used to determine atmospheric total suspended particulate lead dispersion, which originated from stack emissions, over the wider study area. Stack emission parameters were obtained from online measurements of the industry control sensors. AERMOD simulated two scenarios for four calendar years, 2015 to 2018, one for the typical stack measured operating conditions and one for the legal limit operating conditions (emissions from the stack set by legislation to 0.5 mg m−3). Deposition fluxes modeled the input of atmospheric total suspended particulate Pb to the topsoil of the area. X-ray fluorescence (XRF) analyses were used to determine lead concentrations in the topsoil. The modeling results were compared with topsoil of six inhabited locations downwind from the stack in the direction of the prevailing winds to estimate the influence of lead deposition on topsoil near the industrial area.

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AERMOD: A Dispersion Model for Industrial Source Applications. Part I: General Model Formulation and Boundary Layer Characterization

Journal of Applied Meteorology ◽

10.1175/jam2227.1 ◽

2005 ◽

Vol 44 (5) ◽

pp. 682-693 ◽

Cited By ~ 296

Author(s):

Alan J. Cimorelli ◽

Steven G. Perry ◽

Akula Venkatram ◽

Jeffrey C. Weil ◽

Robert J. Paine ◽

...

Keyword(s):

Boundary Layer ◽

Environmental Protection Agency ◽

Rural Areas ◽

Dispersion Model ◽

Sensible Heat Flux ◽

Surface Friction ◽

Obukhov Length ◽

American Meteorological Society ◽

Stable Conditions ◽

Terrain Following

Abstract The formulation of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model is described. This is the first of two articles describing the model and its performance. Part I includes AERMOD’s characterization of the boundary layer with computation of the Monin–Obukhov length, surface friction velocity, surface roughness length, sensible heat flux, convective scaling velocity, and both the shear- and convection-driven mixing heights. These parameters are used in conjunction with meteorological measurements to characterize the vertical structure of the wind, temperature, and turbulence. AERMOD’s method for considering both the vertical inhomogeneity of the meteorological characteristics and the influence of terrain are explained. The model’s concentration estimates are based on a steady-state plume approach with significant improvements over commonly applied regulatory dispersion models. Complex terrain influences are provided by combining a horizontal plume state and a terrain-following state. Dispersion algorithms are specified for convective and stable conditions, urban and rural areas, and in the influence of buildings and other structures. Part II goes on to describe the performance of AERMOD against 17 field study databases.

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Investigations Concerning the Residence Time Distribution of Twin-Screw-Extrusion Processes as Indicator for Inherent Mixing

Pharmaceutics ◽

10.3390/pharmaceutics10040207 ◽

2018 ◽

Vol 10 (4) ◽

pp. 207 ◽

Cited By ~ 4

Author(s):

Jens Wesholowski ◽

Andreas Berghaus ◽

Markus Thommes

Keyword(s):

Residence Time ◽

Mass Flow ◽

Time Distribution ◽

Residence Time Distribution ◽

Dispersion Model ◽

Model Parameters ◽

Twin Screw Extrusion ◽

Characteristic Model ◽

Screw Extrusion ◽

Twin Screw

Over recent years Twin-Screw-Extrusion (TSE) has been established as a platform technology for pharmaceutical manufacturing. Compared to other continuous operation, one of the major benefits of this method is the combination of several unit operations within one apparatus. Several of these are linked to the Residence Time Distribution (RTD), which is typically expressed by the residence time density function. One relevant aspect for pharmaceutical processes is the mixing capacity, which is represented by the width of this distribution. In the frame of this study the influence of the mass flow, the temperature and the screw-barrel clearance were investigated for a constant barrel load (specific feed load, SFL). While the total mass flow as well as the external screw diameter affected the mixing performance, the barrel temperature had no influence for the investigated range. The determined results were additionally evaluated with respect to a fit to the Twin-Dispersion-Model (TDM). This model is based on the superimposition of two mixing functions. The correlations between varied process parameters and the obtained characteristic model parameters proved this general physical view on extrusion.

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Catastrophic Health Impacts of Spiraling Climate Change: How Certain Can We Be About Their Magnitudes?

Frontiers in Public Health ◽

10.3389/fpubh.2020.584721 ◽

2020 ◽

Vol 8 ◽

Author(s):

Zaid Chalabi ◽

Anna M. Foss

Keyword(s):

Climate Change ◽

Human Health ◽

Climate Model ◽

Environmental Exposures ◽

Building Blocks ◽

Health Impacts ◽

Model Parameters ◽

Policy Makers ◽

Total Uncertainty ◽

Human Health Impacts

Recently, there has been a strong interest in the climate emergency and the human health impacts of climate change. Although estimates have been quoted, the modeling methods used have either been simplistic or opaque, making it difficult for policy makers to have confidence in these estimates. Providing central estimates of health impacts, without any quantification of their uncertainty, is deficient because such an approach does not acknowledge the inherent uncertainty in extreme environmental exposures associated with spiraling climate change and related health impacts. Furthermore, presenting only the uncertainty bounds around central estimates, without information on how the uncertainty in each of the model parameters and assumptions contribute to the total uncertainty, is insufficient because this approach hides those parameters and assumptions which contribute most to the total uncertainty. We propose a framework for calculating the catastrophic human health impacts of spiraling climate change and the associated uncertainties. Our framework comprises three building blocks: (A) a climate model to simulate the environmental exposure extremes of spiraling climate change; (B) a health impact model which estimates the health burdens of the extremes of environmental exposures; and (C) an analytical mathematical method which characterizes the uncertainty in (A) and (B), propagates the uncertainty in-between and through these models, and attributes the proportion of uncertainty in the health outcomes to model assumptions and parameter values. Once applied, our framework can be of significant value to policy makers because it handles uncertainty transparently while taking into account the complex interactions between climate and human health.

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Spatial and Seasonal Variations and Inter-Relationship in Fitted Model Parameters for Rainfall Totals across Australia at Various Timescales

Climate ◽

10.3390/cli7010004 ◽

2019 ◽

Vol 7 (1) ◽

pp. 4 ◽

Cited By ~ 1

Author(s):

Md Masud Hasan ◽

Barry F. W. Croke ◽

Fazlul Karim

Keyword(s):

Probabilistic Models ◽

Dispersion Model ◽

Synthetic Data ◽

Daily Rainfall ◽

Seasonal Rainfall ◽

Model Parameters ◽

Exponential Dispersion Model ◽

Gamma Distributions ◽

Using Data ◽

Combined Data

Probabilistic models are useful tools in understanding rainfall characteristics, generating synthetic data and predicting future events. This study describes the results from an analysis on comparing the probabilistic nature of daily, monthly and seasonal rainfall totals using data from 1327 rainfall stations across Australia. The main objective of this research is to develop a relationship between parameters obtained from models fitted to daily, monthly and seasonal rainfall totals. The study also examined the possibility of estimating the parameters for daily data using fitted parameters to monthly rainfall. Three distributions within the Exponential Dispersion Model (EDM) family (Normal, Gamma and Poisson-Gamma) were found to be optimal for modelling the daily, monthly and seasonal rainfall total. Within the EDM family, Poisson-Gamma distributions were found optimal in most cases, whereas the normal distribution was rarely optimal except for the stations from the wet region. Results showed large differences between regional and seasonal ϕ-index values (dispersion parameter), indicating the necessity of fitting separate models for each season. However, strong correlations were found between the parameters of combined data and those derived from individual seasons (0.70–0.81). This indicates the possibility of estimating parameters of individual season from the parameters of combined data. Such relationship has also been noticed for the parameters obtained through monthly and daily models. Findings of this research could be useful in understanding the probabilistic features of daily, monthly and seasonal rainfall and generating daily rainfall from monthly data for rainfall stations elsewhere.

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Sensitivity analysis and the challenges posed by multiple approaches: a multifaceted mess

10.5194/egusphere-egu2020-6626 ◽

2020 ◽

Author(s):

Monica Riva ◽

Aronne Dell'Oca ◽

Alberto Guadagnini

Keyword(s):

Sensitivity Analysis ◽

Model Calibration ◽

Global Sensitivity Analysis ◽

System Model ◽

Model Parameters ◽

Model Output ◽

Multiple Model ◽

Total Uncertainty ◽

Global Sensitivity ◽

Sensitivity Indices

Modern models of environmental and industrial systems have reached a relatively high level of complexity. The latter aspect could hamper an unambiguous understanding of the functioning of a model, i.e., how it drives relationships and dependencies among inputs and outputs of interest. Sensitivity Analysis tools can be employed to examine this issue.Global sensitivity analysis (GSA) approaches rest on the evaluation of sensitivity across the entire support within which system model parameters are supposed to vary. In this broad context, it is important to note that the definition of a sensitivity metric must be linked to the nature of the question(s) the GSA is meant to address. These include, for example: (i) which are the most important model parameters with respect to given model output(s)?; (ii) could we set some parameter(s) (thus assisting model calibration) at prescribed value(s) without significantly affecting model results?; (iii) at which space/time locations can one expect the highest sensitivity of model output(s) to model parameters and/or knowledge of which parameter(s) could be most beneficial for model calibration?The variance-based Sobol&#8217; Indices (e.g., Sobol, 2001) represent one of the most widespread GSA metrics, quantifying the average reduction in the variance of a model output stemming from knowledge of the input. Amongst other techniques, Dell&#8217;Oca et al. [2017] proposed a moment-based GSA approach which enables one to quantify the influence of uncertain model parameters on the (statistical) moments of a target model output.Here, we embed in these sensitivity indices the effect of uncertainties both in the system model conceptualization and in the ensuing model(s) parameters. The study is grounded on the observation that physical processes and natural systems within which they take place are complex, rendering target state variables amenable to multiple interpretations and mathematical descriptions. As such, predictions and uncertainty analyses based on a single model formulation can result in statistical bias and possible misrepresentation of the total uncertainty, thus justifying the assessment of multiple model system conceptualizations. We then introduce copula-based sensitivity metrics which allow characterizing the global (with respect to the input) value of the sensitivity and the degree of variability (across the whole range of the input values) of the sensitivity for each value that the prescribed model output can possibly undertake, as driven by a governing model. In this sense, such an approach to sensitivity is global with respect to model input(s) and local with respect to model output, thus enabling one to discriminate the relevance of an input across the entire range of values of the modeling goal of interest. The methodology is demonstrated in the context of flow and reactive transport scenarios.&#160;ReferencesSobol, I. M., 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Sim., 55, 271-280.Dell&#8217;Oca, A., Riva, M., Guadagnini, A., 2017. Moment-based metrics for global sensitivity analysis of hydrological systems. Hydr. Earth Syst. Sci., 21, 6219-6234.

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Solids Flow Models for Gas - Flowing Solids - Fixed Bed Contactors

International Journal of Chemical Reactor Engineering ◽

10.2202/1542-6580.2228 ◽

2010 ◽

Vol 8 (1) ◽

Author(s):

Nikola M Nikacevic ◽

Milorad P. Dudukovic

Keyword(s):

Dispersion Model ◽

Fixed Bed ◽

Axial Dispersion ◽

Step Response ◽

Model Parameters ◽

Response Curves ◽

Flow Models ◽

Solids Holdup ◽

Axial Dispersion Model ◽

Static Zone

Three solids flow models for gas flowing solids fixed bed contactors are analyzed. They all presume axial dispersion in the dynamic, freely flowing zone, but they differ in the interpretation of the stagnant zone. The models have been examined and the model parameters have been optimized on the basis of two types of tracer experiments. One provides step response curves for flowing solids at the exit and the other presents the response curves of the static flowing solids holdup. The model which assumes axial dispersion and exchange between dynamic and two active static zones, most accurately describes the solids flow pattern. A simpler model which presumes exchange between dynamic and one static zone can be used if there is no need for a precise description of the behavior of stagnant particles. The most simple axial dispersion model is not realistic, as it does not explain stagnancy at all, which was experimentally observed for the gas flowing solids fixed bed contactors.

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A review of sea-spray aerosol source functions using a large global set of sea salt aerosol concentration measurements

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-1277-2014 ◽

2014 ◽

Vol 14 (3) ◽

pp. 1277-1297 ◽

Cited By ~ 104

Author(s):

H. Grythe ◽

J. Ström ◽

R. Krejci ◽

P. Quinn ◽

A. Stohl

Keyword(s):

Wind Speed ◽

Source Function ◽

Climate Models ◽

Dispersion Model ◽

Cloud Condensation Nuclei ◽

Source Region ◽

Particle Dispersion ◽

Sea Spray ◽

Observation Data ◽

Best Fit

Abstract. Sea-spray aerosols (SSA) are an important part of the climate system because of their effects on the global radiative budget – both directly as scatterers and absorbers of solar and terrestrial radiation, and indirectly as cloud condensation nuclei (CCN) influencing cloud formation, lifetime, and precipitation. In terms of their global mass, SSA have the largest uncertainty of all aerosols. In this study we review 21 SSA source functions from the literature, several of which are used in current climate models. In addition, we propose a~new function. Even excluding outliers, the global annual SSA mass produced spans roughly 3–70 Pg yr−1 for the different source functions, for particles with dry diameter Dp < 10 μm, with relatively little interannual variability for a given function. The FLEXPART Lagrangian particle dispersion model was run in backward mode for a large global set of observed SSA concentrations, comprised of several station networks and ship cruise measurement campaigns. FLEXPART backward calculations produce gridded emission sensitivity fields, which can subsequently be multiplied with gridded SSA production fluxes in order to obtain modeled SSA concentrations. This allowed us to efficiently and simultaneously evaluate all 21 source functions against the measurements. Another advantage of this method is that source-region information on wind speed and sea surface temperatures (SSTs) could be stored and used for improving the SSA source function parameterizations. The best source functions reproduced as much as 70% of the observed SSA concentration variability at several stations, which is comparable with "state of the art" aerosol models. The main driver of SSA production is wind, and we found that the best fit to the observation data could be obtained when the SSA production is proportional to U103.5, where U10 is the source region averaged 10 m wind speed. A strong influence of SST on SSA production, with higher temperatures leading to higher production, could be detected as well, although the underlying physical mechanisms of the SST influence remains unclear. Our new source function with wind speed and temperature dependence gives a global SSA production for particles smaller than Dp < 10 μm of 9 Pg yr−1, and is the best fit to the observed concentrations.

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Optical phonons and free-carrier effects in MOVPE grown AlxGa1−xN measured by Infrared Ellipsometry

MRS Internet Journal of Nitride Semiconductor Research ◽

10.1557/s1092578300000673 ◽

1999 ◽

Vol 4 (1) ◽

Cited By ~ 46

Author(s):

M. Schubert ◽

A. Kasic ◽

T.E. Tiwald ◽

J. Off ◽

B. Kuhn ◽

...

Keyword(s):

Dispersion Model ◽

Free Carrier ◽

Model Parameters ◽

Model Calculations ◽

Group Iii ◽

Infrared Ellipsometry ◽

Mode Behavior ◽

Novel Approach ◽

Back Ground ◽

Group Iii Nitride

We report on the application of infrared spectroscopic ellipsometry (IR-SE) for wavenumbers from 333cm−1 to 1200cm−1 as a novel approach to non-destructive optical characterization of free-carrier and optical phonon properties of group III-nitride heterostructures. Undoped α-GaN, α-AlN, α-AlxGa1−xN (x = 0.17, 0.28, 0.5), and n-type silicon (Si) doped α-GaN layers were grown by metal-organic vapor phase epitaxy (MOVPE) on c-plane sapphire (α-Al2O3). The four-parameter semi-quantum (FPSQ) dielectric lattice-dispersion model and the Drude model for free-carrier response are employed for analysis of the IR-SE data. Model calculations for the ordinary (∈⊥) and extraordinary (∈||) dielectric functions of the heterostructure components provide sensitivity to IR-active phonon frequencies and free-carrier parameters. We observe that the α-AlxGa1−xN layers are unintentionally doped with a back ground free-carrier concentration of 1–4 1018cm−3. The ternary compounds reveal a two-mode behavior in ∈⊥, whereas a one-mode behavior is sufficient to explain the optical response for ∈||. We further provide a precise set of model parameters for calculation of the sapphire infrared dielectric functions which are prerequisites for analysis of infrared spectra of III-nitride heterostructures grown on α-Al2O3.

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