Managing emissions from highly industrialized areas: Regulatory compliance under uncertainty

El‐Fadel, Mutasem; Abi-Esber, L.; Ayash, T.

doi:10.1016/j.atmosenv.2009.06.056

Cited by 14 publications

(15 citation statements)

References 21 publications

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“…Whenever Tier 2 emission factors were used, emissions from storage, handling, and transport of product were calculated separately and added to the final emission factor (this type of emissions is implicitly accounted for in Tier 1 emission factors). In the case of cement and lime and plaster production, 99%, 95%, and 90% reduction in minimum, average, and maximum PM 10 emission factors were considered because the EEA emission factors assume that only ESP control is in place, whereas field interviews with the industries showed that bag house filters are also being used (El-Fadel et al, 2009). Similarly, 99%, 95%, and 80% reduction in minimum, average, and maximum SO 2 emission factors from the chemical industry with sulfuric acid production were considered because the EEA emission factors do not assume any type of controls while a wet scrubbing process is reportedly in place (El-Fadel et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Atmospheric dispersion modeling is commonly applied for air quality assessment and for testing the effectiveness of management options prior to their implementation and providing forecasts of future air quality under various "what if " scenarios (El-Fadel et al, 2009). In this context, the main modeling approaches encompass box, Gaussian, Lagrangian, and computational fluid dynamics (CFD) models, with the Gaussian modeling approach having the most widespread use in regulatory contexts.…”

Section: Introductionmentioning

confidence: 99%

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Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

El‐Fadel

Abi-Esber

2012

Journal of the Air & Waste Management Association

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In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NO X ), carbon monoxide (CO), fine particulate matter (PM 10 ) and sulfur dioxide (SO 2 )) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NO X , CO, PM 10 and SO 2 , respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.Implications: In the absence of site-specific source emission factors, the use of internationally reported emission factors without testing their validity may generate significant errors. Instead, recorded field measurements and meteorological data may be combined with atmospheric transport and dispersion models to better estimate source emissions, particularly in regulatory compliance studies. In this context, lower model performance is expected at higher wind speeds for most indicators such as CO, PM 10 , and SO 2 .

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

El‐Fadel

Abi-Esber

2012

Journal of the Air & Waste Management Association

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“…Complex topographical structure of the study area compared to the performance quality of the Gaussian modellings at plain and flat terrains causes uncertainty in the modelling results [42]. This is because steady-state Gaussian models accept that transport varies linearly as a function of time and space.…”

Section: Evaluation Of Model Performancesmentioning

confidence: 99%

Determining Performance and Application of Steady-State Models and Lagrangian Puff Model for Environmental Assessment of CO and NOx Emissions

Demirarslan¹,

Doğruparmak²

2016

Pol. J. Environ. Stud.

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Air quality modellings are highly useful systems used to investigate the possible impact of emissions diffusing into the atmosphere in any area they might have on that area. There are many modelling methods whose capacities are limited by their advantages and disadvantages or the equipment they use. In this study, therefore, both steady-state models (AERMOD and ISCST-3) and the Lagrangian model (CALPUFF) are used. This study has two purposes: one is to specify performance of the models. Performances were determined with various statistical methods such as fractional bias (FB), mean squared error (MSE), and geometric mean bias (MG). The other purpose of this study is to evaluate temporal and spatial variations of point (P), area (A), and line (L) -sourced CO and NO x emissions in the research area by using the modelling methods. The district of Körfez, which is one of the districts of the province of Kocaeli, was chosen as the study area.When the results obtained with modelling all P and A sources by three programs are analyzed, the highest annual concentration AERMOD, ISCST-3, and CALPUFF were found as 128.82, 86.96, and 201.30 µg/m 3 for CO, and 7.56, 26.31, and 6.10 µg/m 3 for NO x , respectively. On the other hand, when the results obtained with modelling all P and A and L sources by two programs are investigated, the highest annual concentration AERMOD and ISCST-3 were found to be 155.12, 92.46 µg/m 3 for CO, and 166.93 and 89.98 µg/m 3 for NO x , respectively. When contributions of the pollutant sources on pollution are evaluated, it was observed that area sources and line sources are more predominant than other sources for CO and NO x emissions. It was observed by analyzing the diffusion maps that residential areas in the district are more concentrated. Therefore, in the study the predicted and observed values were also compared with national and international limit values and determined to meet these limit values. According to the results obtained by evaluation of performances of the models with FB, MS, and MG statistical methods, performance sorting for NO x emissions was found to be ISCST-3 > CALPUFF > AERMOD, while for CO emissions it is given as

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Section: Emission Factorsmentioning

confidence: 99%

“…Nevertheless, industrial emissions in urban areas continue to be under scientific scrutiny, given their association with aggravated air quality in these areas (Krishna et al, 2005;El-Fadel et al, 2009;El-Fadel and Abi Esber, 2012), and their relative contribution to ambient air pollution assists decision-makers in implementing cost-effective management plans . However, working from measured pollutant concentrations at a particular receptor back to emissions requires an understanding of those processes that dictate plume evolution and transport, namely, the release height, meteorological factors, and chemical transformations.…”

Section: Introductionmentioning

confidence: 99%

A framework for emissions source apportionment in industrial areas: MM5/CALPUFF in a near-field application

Ghannam

El‐Fadel

2012

Journal of the Air & Waste Management Association

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This paper examines the relative source contribution to ground-level concentrations of carbon monoxide (CO), nitrogen dioxide (NO 2 ), and PM 10 (particulate matter with an aerodynamic diameter <10 m) in a coastal urban area due to emissions from an industrial complex with multiple stacks, quarrying activities, and a nearby highway. For this purpose, an inventory of CO, oxide of nitrogen (NO x ), and PM 10 emissions was coupled with the non-steady-state Mesoscale Model 5/California Puff Dispersion Modeling system to simulate individual source contributions under several spatial and temporal scales. As the contribution of a particular source to ground-level concentrations can be evaluated by simulating this single-source emissions or otherwise total emissions except that source, a set of emission sensitivity simulations was designed to examine if CALPUFF maintains a linear relationship between emission rates and predicted concentrations in cases where emitted plumes overlap and chemical transformations are simulated. Source apportionment revealed that ground-level releases (i.e., highway and quarries) extended over large areas dominated the contribution to exposure levels over elevated point sources, despite the fact that cumulative emissions from point sources are higher. Sensitivity analysis indicated that chemical transformations of NO x are insignificant, possibly due to short-range plume transport, with CALPUFF exhibiting a linear response to changes in emission rate. The current paper points to the significance of ground-level emissions in contributing to urban air pollution exposure and questions the viability of the prevailing paradigm of point-source emission reduction, especially that the incremental improvement in air quality associated with this common abatement strategy may not accomplish the desirable benefit in terms of lower exposure with costly emissions capping.Implications: The application of atmospheric dispersion models for source apportionment helps in identifying major contributors to regional air pollution. In industrial urban areas where multiple sources with different geometry contribute to emissions, ground-level releases extended over large areas such as roads and quarries often dominate the contribution to groundlevel air pollution. Industrial emissions released at elevated stack heights may experience significant dilution, resulting in minor contribution to exposure at ground level. In such contexts, emission reduction, which is invariably the abatement strategy targeting industries at a significant investment in control equipment or process change, may result in minimal return on investment in terms of improvement in air quality at sensitive receptors.

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Managing emissions from highly industrialized areas: Regulatory compliance under uncertainty

Cited by 14 publications

References 21 publications

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

Determining Performance and Application of Steady-State Models and Lagrangian Puff Model for Environmental Assessment of CO and NOx Emissions

A framework for emissions source apportionment in industrial areas: MM5/CALPUFF in a near-field application

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