Source Complex Short Term Version Research Articles

Evaluation of Pollutants Along the National Road N2 in Togo using the AERMOD Dispersion Model.

Background.Air pollution has become a major problem around the world and is increasingly an issue in Togo due to increased vehicular traffic. Gaseous pollutants are released by engines and are very harmful to human health and the environment. The fuels used on the major road in Togo, the N2, are adulterated with unknown contents and are of poor quality. Many of the vehicles come from neighboring countries, such as Benin, Ghana and Nigeria.Objectives.The present study aims to evaluate the pollution rate in Togo through the estimation of the concentrations of sulfur dioxide (SO2), nitrogen oxides (NOx), and particular matter (PM) on the international road, the National Road N2, in Lomé, compared to the World Health Organization's (WHO) standard limit.Methods.The simulations of pollutant concentration were performed using the Industrial Source Complex Short Term Version 3 model, which is included in the United States Environmental Protection Agency Regulatory Model (USEPA) AERMOD View software. The meteorological averages data were obtained from the local station near the National Road N2 in Togo in 2018. Hourly averages were calculated according to the European Monitoring Evaluation Programme/European Environmental Agency air pollutant emission inventory guidebook 2016 and were processed using AERMET View and a terrain pre-processor, AERMAP. For the model, the sources of pollution were the vehicles traveling on the road segment. The source was a line volume with 20 m of width and 2 m of height. The estimation methodology covered exhaust emissions of NOx, SO2 and PM contained in the fuel.Results.The simulations provided average hourly, daily and annual concentrations of the different pollutants: 71.91 μg/m3, 42.41 μg/m3,11.23 μg/m3 for SO2; 16.78 μg/m3, 9.89 μg/m3, 2.46 μg/m3 for NOx and below the detection limit, 0.62 μg/m3, 0.15 μg/m3 for PM, respectively. These results indicate that on the National Road N2 in Togo, the concentrations of SO2 were high compared to those of NOx and PM. The daily average concentration of SO2 was twice the permissible limits set by the WHO.Conclusions.Emissions obtained from the AERMOD for NOx and PM were less than the permissible limits set by the WHO, while the rate of SO2 was twice the permissible limit. The fuels used on this road were very rich in sulfur. The sulfur level in fuels must be monitored by stakeholders in Togo.Competing Interests.The authors declare no competing financial interests.

Comparative Study of Air Pollution Modeling Techniques from Point Source(s) of Thermal Power Plant

In the present study, air quality dispersion analysis was performed for the area near the Rajpura Thermal Power Plant, located in the Punjab province of India. The emission inventory was prepared from point source emissions of the power plant and two representative large-scale industries to differentiate the impact of the thermal power plant alone. Prediction of ground-level concentrations of air pollutants viz. SO2, NO2, and SPM was performed by the United States Environment Protection Agency‑approved air quality dispersion models, namely Industrial Source Complex-Short Term Version 3 (ISCST3) and American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD). The spatial dispersion of maximum concentration for the sources of thermal power plant showed that AERMOD predicted higher concentrations near the source when compared with ISCST3 for daily averaging periods. The predicted ground-level concentrations of both the models with data from all the sources’ inventory were found to be similar to the predicted ground-level concentrations of both the models with inputs limited to the source emission data of the thermal power plant alone. The predicted concentrations by two models were compared with each other and validation of models was done with the actual observed concentrations at eight major receptor sites within the study area for the better model performance. The coefficient of correlation graphs suggested that the overall results of AERMOD predictions were better than those of ISCST3 predictions.

Prediction of Ground Level Concentrations of No<sub>x</sub> in a Thermal Power Project using ISCST3 Model

Background: Present study deals with the prediction of nitrogen oxides in the vicinity of the Rayalaseema Thermal Power Project, Andhra Pradesh, India. It provides information to the public and the control agencies to efficiently implement the control strategies of the air quality management program. Methods: In this prediction, the industrial source complex short-term version 3 (ISCST3) model was used. ISCST3 is the preferred model of USEPA. ISCST3 is a refined dispersion modeling technique using site specific input data. Findings: Predictions were made around the power plant with the radius of 10,000 meters; for the period of one year from June 2012 to May 2013. ISCST3 and Golden software surfer version 8 was used to produce Isopleths and 3-Dimensional view of NOX in the vicinity of the Rayalaseema Thermal Power Project. Similarity beside measurements of Ambient Air Quality Monitoring Stations (AAQMS) was made. Genuine results were found well with measured data. Coefficients of determination (R2) were found in the range between 0.76 to 0.96. The ISCST3 model can be used for NOX prediction with good accuracy. Application and Improvement: Further forecasting can be carried out in coal fired power stations for using wide ranging parameters, which will have significant impact on human health and environment using ISCST3 modeling technique.

NOX EMISSION MODELLING FROM INDUSTRIAL STEAM BOILERS

Previous researches often emphasize on the sources and effects of air pollutants in the environment and human population. A part of those studies were done in order to explore the spread or distribution pattern of those pollutants, especially regarding the emission from industrial steam boilers. It is very important to evaluate the transfer trend of air pollutants at both local and global scales. In this study, Industrial Source Complex Short Term Version 3 (ISCST3) model has been used to predict the distribution of NOx emitted from industrial steam boilers in the District of Hulu Langat, Selangor. The result of analyses indicates that the emission rates for steam boilers were ranging from 0.0083 kg NOx/hour to as high as 0.2771 kg NOx/hour while the total emission load was 1.9969 kg NOx­/hour. The evaluation on dispersion contour shows that the concentration of NOx was higher in 1-hour reading than 24-hour value. The highest concentration of NOx was predicted to be within 104.65 km2 from the sources of NOx (steam boilers).

Particulate Matter Emission Factors for Almond Harvest as a Function of Harvester Speed

Almond harvest accounts for substantial particulate matter less than 10 µm in aerodynamic diameter (PM10) emissions in California each harvest season. This paper addresses the reduction of harvester ground speed from a standard 8 km/hr (5 mph) to 4 km/hr (2.5 mph) as a possible mitigation measure for reducing PM10 emissions. Ambient total suspended particulate (TSP) and PM10 sampling was conducted during harvest with alternating control (8 km/hr [5 mph]) and experimental (4 km/hr [2.5 mph]) treatments. On-site meteorological data were used in conjunction with both Industrial Source Complex-Short Term version 3 (ISCST3) and the American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD) dispersion models to back-calculate emission rates from the measured concentrations. Baseline annual emission factors for nut pickup of 381 ± 122 and 361 ± 123 kg PM10/km2·yr were determined using ISCST3 and AERMOD, respectively. Both of these values are substantially lower than the current PM10 emission factor for almond pickup of 4120 kg PM10/km2·yr. The particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) emission factors for nut pickup developed from this study were 25 ± 8 kg PM2.5/km2·yr and 24 ± 8 kg PM10/km2·yr were determined using ISCST3 and AERMOD, respectively. Reducing harvester speed resulted in an emissions reduction of 42% for TSP, but no differences were detected in emissions of PM10 and PM2.5. Differences detected in the emission factors developed using ISCST3 and AERMOD were not statistically significant, indicating that almond harvest emission factors previously developed using ISCST3 may be applied appropriately in AERMOD.

Concentrations, Profiles, and Sources of Atmospheric PCDD/Fs near a Municipal Solid Waste Incinerator in Eastern China

Ambient air monitoring of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) was performed to investigate their concentrations, profiles, and sources near a municipal solid waste incinerator (MSWI) in Eastern China. The PCDD/F concentrations varied over a factor of 51.4 from 0.059 to 3.03 pg I-TEQ m(-3) (or over 41.4 from 3.96 to 164 pg m(-3)) and fell at the higher end of the range in recorded peer investigations around the world. Almost all ambient air samples show a unique TCDF-dominanthomologue pattern, characterized by decreasing concentrations with increasing levels of chlorination (except for OCDD). By contrast, the dominant congener in terms of concentration profiles shows temporal and spatial variations among the OCDD, OCDF, and 1,2,3,4,6,7,8-HpCDF. The results from a congener-specific factor analysis between ambient air and PCDD/F emission sources, as well as U.S. EPA Chemical Mass Balance (CMB8.2) and Industrial Source Complex Short Term Version 3 (ISCST3) modeling, suggest that the deterioration of air quality in the study area is primarily attributed to open burning of wastes (OB), followed by hot water boilers (HWBs), traffic, and the MSWI.

Prediction of ground level concentration of sulfur dioxide using ISCST3 model in Mangalore industrial region of India

Ambient air quality management in any industrial area is of prime concern in India considering the industrial growth since last two decades. High concentrations of ambient sulfur dioxide (SO2) in many Indian places are responsible for non-compliance of ambient air quality standards. Dispersion modeling finds an important tool to simulate the ambient air quality of a region and to predict the ground level concentration of SO2 under various scenarios. Mangalore region covering a wide range of mountains is chosen in the present investigation for the application of a widely used Industrial Source Complex—Short Term Version 3 (ISCST3) model to predict the ground level concentration of SO2 due to a proposed thermal power plant along with other existing industries. In fact, the COMPLEX1 screening model dispersion algorithms of ISCST3 are used for estimating the ground level concentration patterns for receptors in complex terrain (i.e., terrain above stack height or release height) besides estimation for receptors in simple terrain so as to accurately model the dispersion phenomena in the presence of mountains located in the study area well above the stationary industrial point sources. Impacts of industrial establishments under various scenarios as also the influence of mountains in the study area on the ground level concentration patterns are analyzed in details. Precise model calculations leading to ground level concentration distribution pattern revealed many peaks in both flat and hilly terrains. Such dramatic behavior was explained by the back propagation of the emitting waves after these were caused to bounced back by hitting the mountains and subsequently modified the incoming emitting waves. Model calculations further showed that incorporation of SO2 emission control measures, for instance, implementation of flue gas desulfurization facilities having at least 90% removal efficiency, to selected stacks would result in ambient SO2 concentration well within the prescribed values under Indian condition both in simple and in complex terrains and would thus curb air pollution in the study area. Any policy recommendation, based on the three considerations in the presented study that led to conservative values of ground level concentrations of SO2, would be realistic for addressing the proper air quality management in the study area and also it would help in guiding the regulatory authority not only for the present case but also for similar other future situations. In the light of these observations, finally a few policy recommendations are put forward for the policy makers and regulatory authority.

Sensitivity of Two Dispersion Models (AERMOD and ISCST3) to Input Parameters for a Rural Ground-Level Area Source

As of December 2006, the American Meteorological Society/U.S. Environmental Protection Agency (EPA) Regulatory Model with Plume Rise Model Enhancements (AERMOD-PRIME; hereafter AERMOD) replaced the Industrial Source Complex Short Term Version 3 (ISCST3) as the EPA-preferred regulatory model. The change from ISCST3 to AERMOD will affect Prevention of Significant Deterioration (PSD) increment consumption as well as permit compliance in states where regulatory agencies limit property line concentrations using modeling analysis. Because of differences in model formulation and the treatment of terrain features, one cannot predict a priori whether ISCST3 or AERMOD will predict higher or lower pollutant concentrations downwind of a source. The objectives of this paper were to determine the sensitivity of AERMOD to various inputs and compare the highest downwind concentrations from a ground-level area source (GLAS) predicted by AERMOD to those predicted by ISCST3. Concentrations predicted using ISCST3 were sensitive to changes in wind speed, temperature, solar radiation (as it affects stability class), and mixing heights below 160 m. Surface roughness also affected downwind concentrations predicted by ISCST3. AERMOD was sensitive to changes in albedo, surface roughness, wind speed, temperature, and cloud cover. Bowen ratio did not affect the results from AERMOD. These results demonstrate AERMOD’s sensitivity to small changes in wind speed and surface roughness. When AERMOD is used to determine property line concentrations, small changes in these variables may affect the distance within which concentration limits are exceeded by several hundred meters.

Application of the ISCST3 model for predicting PCDD/F concentrations in agricultural soil in the vicinity of a MSWI plant in China

Based on the Industrial Source Complex Short-Term Version 3 (ISCST3) model, a simplified modeling approach was developed to predict concentrations of congeners of polychlorinated-p-dioxins and dibenzofurans (PCDD/Fs) of agricultural soil, within a radius of 3 km from a municipal solid waste incinerator (MSWI) plant after its 4-year operation in Hangzhou, China. Comparisons were made between the measured and estimated congener-specific concentrations and the international-toxic equivalent (I-TEQ) values of soil samples with respect to distance from the stack. The results indicate that the predictions of soil PCDD/F concentrations and I-TEQ values were generally lower than their observations, and that the higher the degree of under-estimation seems, the greater the further downwind one gets. Nevertheless, most of the predictions were in good agreement with the trend of measured ones and were within a factor of ten for samples located within 1 km of the plant. Besides, analysis of contributions of various deposition pathways confirms that in addition to wet particle deposition, the dry gaseous deposition is essential for realistic prediction of PCDD/F depositions to soil, especially for tetra- and penta-chlorinated dioxins.

Comparison of Dispersion Models for Ammonia Emissions from a Ground-Level Area Source

Dispersion models are important tools for determining and regulating pollutant emissions from many sources, including ground-level area sources such as feedyards, dairies, and agricultural field operations. This study compares the calculated emission fluxes of ammonia from a feedyard in the Texas panhandle using four dispersion models: Industrial Source Complex Short Term Version 3 (ISCST3), AERMOD-PRIME, WindTrax, and AUSTAL. ISCST3 and AERMOD are Gaussian plume models, while WindTrax and AUSTAL are backward and forward Lagrangian stochastic models, respectively. Identical measured downwind ammonia concentration data were entered into each model. The results of this study indicate that calculated emission rates and/or emission factors are model specific, and no simple conversion factor can be used to adjust emission rates and/or factors between models. Therefore, emission factors developed using one model should not be used in other models to determine downwind pollutant concentrations.

CFD prediction of cooling tower drift

Drift of small water droplets from mechanical and natural draft cooling tower installations can contain water treatment chemicals such that contact with plants, building surfaces and human activity can be hazardous. Prediction of drift deposition is generally provided by analytic models such as the US Environmental Protection Agency approved Industrial Source Complex Short Term Version 3 (ISCST3) or Seasonal-Annual Cooling Tower Impact (SACTI) codes; however, these codes are less suitable when cooling towers are located midst taller structures and buildings. A computational fluid dynamics (CFD) code including Lagrangian prediction of the gravity driven but stochastic trajectory descent of droplets is considered and compared to data from the 1977 Chalk Point Dye Tracer Experiment. The CFD program predicts plume rise, surface concentrations, plume centerline concentrations and surface drift deposition within the bounds of field experimental accuracy.

입력변수의 조건에 따른 대기확산모델의 민감도 분석

In order to how well predict ISCST3(lndustrial Source Complex Short Term version 3) model dispersion of air pollutant at point source, sensitivity was analysed necessary parameters change. ISCST3 model is Gaussian plume model. Model calculation was performed with change of the wind speed, atmospheric stability and mixing height while the wind direction and ambient temperature are fixed. Fixed factors are wind direction as the south wind(l80") and temperature as 298 K(25 "C). Model's sensitivity is analyzed as wind speed, atmospheric stability and mixing height change. Data of stack are input by inner diameter of 2m, stack height of 30m, emission temperature of 40 "C, outlet velocity of 10m/s. On the whole, main factor which affects in atmospheric dispersion is wind speed and atmospheric stability at ISCST3 model. However it is effect of atmospheric stability rather than effect of distance downwind. Factor that exert big influence in determining point of maximum concentration is wind speed. Meanwhile, influence of mixing height is a little or almost not.

A SIMULATED APPROACH TO ESTIMATING PM10 AND PM2.5 CONCENTRATIONS DOWNWIND FROM COTTON GINS

Cotton gins are required to obtain operating permits from state air pollution regulatory agencies (SAPRA), whichregulate the amount of particulate matter that can be emitted. Industrial Source Complex Short Term version 3 (ISCST3) isthe Gaussian dispersion model currently used by some SAPRAs to predict downwind concentrations used in the regulatoryprocess in the absence of field sampling data. The maximum ambient concentrations for PM10 and PM2.5 are set by theNational Ambient Air Quality Standard (NAAQS) at 150 .g/m3 and 65 .g/m3 (24 h average), respectively. Some SAPRAs usethe NAAQS concentrations as property line concentrations for regulatory purposes. This article reports the results of a uniqueapproach to estimating downwind PM10 and PM2.5 concentrations using Monte Carlo simulation, the Gaussian dispersionequation, the Hino power law, and a particle size distribution that characterizes the dust typically emitted from cotton ginexhausts. These results were then compared to a 10 min concentration (C10) and the concentrations that would be measuredby an FRM PM10 and PM2.5 sampler. The total suspended particulate (TSP) emission rate, particle size distributions, andsampler performance characteristics were assigned to triangular distributions to simulate the real-world operation of the ginand sampling systems. The TSP emission factor given in AP-42 for cotton gins was used to derive the PM mass emission ratefrom a 40 bale/h plant. The Gaussian equation was used to model the ambient TSP concentration downwind from the gin.The performance characteristics for the PM10 and PM2.5 samplers were then used to predict what the measured concentrationwould be for two PSD conditions. The first PSD assumption was that the mass median diameter (MMD) and geometricstandard deviation (GSD) were constant at 12 .m and 2, respectively, and the second scenario assigned a triangulardistribution to the MMD and GSD of {15, 20, 25} .m and {1.8, 2.0, 2.2}, respectively. The results show that the PM2.5 fractionof the dust emitted under either PSD condition was negligible when compared to the NAAQS for PM2.5 of 65 .g/m3. The resultsalso demonstrate that correcting for wind direction changes within the hour using the power law reduces the ambientconcentration by a factor of 2.45.

Modeling of the BTX Species Based on an Emission Inventory of Sources at the Map Ta Phut Industrial Estate in Thailand

Air emissions from the Map Ta Phut petrochemical complex in southeastern Thailand were studied. One focus of the research is the development of an emission inventory of the volatile organic compounds, benzene, toluene, and xylene (BTX). Emission sources were identified and the loading from all mobile and stationary sources were compiled. Based on this data set, the Industrial Source Complex Short-Term version 3 (ISCST-3) was used to predict the contributions of the BTX species, which then were compared with the field measurements. The methodology of the emission inventory is described and evaluated with the shortcomings and areas of uncertainty. The relative importance of point, line and area sources and the influence of the local meteorology on the timing and place of concentration impacts are discussed. The coincidences and differences observed highlight the relative importance of source type and location, and of fugitive emissions and episode events. In particular, this research highlights the importance of area emissions from the petrochemical and petroleum industries, such as the oil refineries and tank farms, and identifies them as potential major contributors to the ambient BTX concentrations.