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Study domain in a radius of 5 km from Maptaphut industrial area (yellow dots represent the position of receptors; red dots represent the position of point sources)
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Assimilative capacities of sulfur dioxide (SO2) and oxides of nitrogen (NOx) in the largest petrochemical industrial complex in Thailand were evaluated in this study. AERMOD dispersion model was simulated to compute for ground level concentrations and spatial distributions of SO2 and nitrogen dioxide (NO2) within a radius of 5 km from Maptaphut ind...
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... This study used the recent five years to substitute the incomplete data on the latest year of emission inventory (EI) on behalf of the Industrial Estate Authority of Thailand (IEAT) to identify and estimate industrial source emissions. For on-road transportation, emission line sources data comprised of 11 roads based on emission factors and related parameters from other studies Thawonkaew et al. 2016). Unfortunately, the number of vehicles data are available only on two (R11 and R12) highways retrieved from the Bureau of Highway Safety (BHS) in 2019. ...
The Maptaphut industrial area, one of the largest petrochemical complexes in Thailand, is the major cause of the various air pollutants. The larger concern is that a significant volume of air pollution is emitted and that air quality management needs to be improved. This is in part due to a lack of deeper understanding of how anthropogenic emissions are emitted from different sources in this area— especially volatile organic compounds (VOCs). Moreover, it has complicated relationship results of air pollution, disease mechanisms, and health effects. As a result, its available data can only give a rough indication of them. These factors are often assumed to be associated with economic consequences, but assessing the health-related economic losses caused by air pollution remains limited in many ways.
Four targeted VOCs were analyzed, including benzene, 1,3-butadiene, 1,2-dichloroethane, and vinyl chloride from industrial and non-industrial sources, namely stacks, flares, storage tanks, wastewater treatment plants, transportation and marketing, fugitive losses, slurry/open equipment/vessel, and on-road mobile emissions. Source apportionment can be conducted using emissions inventory (EI) to establish pollution source databases, the dispersion model, and then imported on the risk model by determining receptors. The AERMOD dispersion model coupled with the IRAP-h view model was used to predict the spatial distribution of the ground-level concentration and analyze the inhalation health risk covering cancer and non-cancer risks— as well as the prioritization of pollutants.
The risk assessment results indicated that the highest risk occurred most from 1,3-butadiene for cancer and chronic non-cancer risks contributed to fugitive sources, about 83% and 94%, and most benzenes for acute non-cancer risk contributed to on-road mobile sources, at about 56%.
Consequently, the benzene classified as the most important priority depending on its risk results, comprehensive epidemiological studies, and discharge volumes.
With the economic benefits assessment, BenMAP-CE was further utilized to estimate the health impacts and economic value of multiple scenarios to facilitate decision-making for benzene reduction. Overall, the 10% rollback policy for benzene concentration, monetized value of about 13.13 billion US dollars for all mortalities, gave the best practical scenario for the most economically viable option based on the B/C (benefit/cost) ratio results in Maptaphut. Ultimately, policymakers need to take additional measures to improve air quality and reduce health impacts while also considering economic benefits, especially benzene reduction.
... The atmospheric stability is affected by solar insulation, mean wind speed and temperature profile [1]. The assessment of assimilative capacity of region for air environment helps in devising region-based plans for mitigation of poor air quality [8]. The assimilative capacities assessment was done for different regions in India like Delhi [9,10], Manali [11] Visakhapatnam [2], Gangtok [12], Kochi [13]. ...
... The research results show that the maximum emissions of PM10, SO 2 and CO were 0.0025; 0.0031 and 0.0075 kg/ha/day, respectively [4]. Apiwat Thawonkaew et al. (2016) calculated the maximum emission load for Thailand's largest oil and gas industrial park -Maptaphut that used the AERMOD model to simulate air quality. Results indicated that the maximum SO 2 emissions can be increased by about 130%, and NOx should be reduced by at least 40% of the current levels, [5]. ...
... Apiwat Thawonkaew et al. (2016) calculated the maximum emission load for Thailand's largest oil and gas industrial park -Maptaphut that used the AERMOD model to simulate air quality. Results indicated that the maximum SO 2 emissions can be increased by about 130%, and NOx should be reduced by at least 40% of the current levels, [5]. Smaranika Panda et al. (2017) calculated the maximum emissions potential in Manali industrial zone, India, using the AERMOD model to simulate air quality. ...
Air pollution in major cities of developing countries is a matter of great concern for managers, scientists, and people. In recent years, many studies have been done to simulate and forecast air quality for big cities in Vietnam as well as in the world with many air quality models have been used. However, studies using air quality models to evaluate the capacity of receiving air emission load in the atmospheric environment in local scale have not been carried out, especially in Vietnam. Therefore, the objective of this study is to assess load-carrying capacity in the atmospheric environment on a local scale for a smaller city at Mekong Delta, with a case study of Can Tho city, Vietnam. The FVM-TAPOM model system was established for the study area with the smallest grid resolution of 2km x 2km. The study results show that the atmospheric environment in Can Tho city still can receive more air emissions according to two seasons of the year (dry and rainy seasons) which are different depending on the seasonal wind direction. The central districts of Can Tho city (Ninh Kieu, Cai Rang, Binh Thuy, O Mon, and Thot Not) can only receive a smaller amount of emissions compared to the others (Vinh Thanh, Co Do, Thoi Lai, and Phong Dien). The amount of air emissions that can be received at the central districts is as follows: CO from 82,000 to 172,000 tons/year/district (696 – 2,142 tons/year/km2); SO2 from 3,800 to 4,900 tons/year/district (31 – 56 tons/year/km2); NOx from 217 to 328 tons/year/district (1.8 – 3.4 tons/year/km2). Similarly, the remaining districts can be received the emission is 164,000 – 653,000 tons of CO/year/district (1,308 – 2,555 tons/year/km2); 5,500 – 7,300 tons of SO2/year/district (17 – 29 tons/year/km2) and 31,000 – 44,000 tons of NOx/year/district (77 – 147 tons/year/km2).
... Hourly computed VC showed a similar diurnal trend in all the three seasons. Various researchers have calculated VC to understand the assimilative potential of the atmosphere (Krishna et al., 2004;Goyal et al., 2005;Rao, 2007, Thepanondh andJitbantoung, 2014;Thawonkaew et al., 2016). With an increase in solar insolation, as the day advances VC also increases reaching a maximum value during afternoon hours. ...
... January 275 325 11 22 419 320 400 12 29 454 February 225 320 13 23 381 300 350 12 28 438 March 225 300 12 22 381 250 300 13 27 400 April 200 300 11 26 362 250 310 15 28 400 May 225 300 11 25 381 300 250 15 25 438 June 225 300 13 30 381 275 325 16 26 419 July 200 300 11 24 362 260 320 12 25 408 August 200 300 11 22 362 260 330 13 27 408 September 220 320 11 23 377 280 310 14 30 423 October 220 300 12 27 377 260 350 13 29 408 November 230 310 13 32 385 300 375 12 28 438 December 265 325 12 30 412 310 400 13 31 446 January 320 375 10 21 454 320 400 14 26 454 February 240 350 12 24 392 300 350 14 27 438 March 22 300 11 25 250 350 330 13 27 477 April 200 280 12 24 362 250 375 14 30 400 May 220 300 12 20 377 250 375 15 32 400 June 220 300 14 22 377 260 400 14 26 408 July 200 300 11 23 362 300 325 11 32 438 August 200 300 13 25 362 280 350 12 28 423 September 200 300 11 24 362 275 375 15 26 419 October 180 300 12 26 346 300 380 13 30 438 November 190 270 12 24 354 310 370 16 27 446 December 220 350 12 25 377 320 400 13 25 454 January 260 340 11 21 408 350 450 15 32 477 February 250 310 12 21 400 300 450 14 35 438 March 260 320 11 22 408 350 550 12 28 550 April 250 320 12 24 400 300 450 16 37 438 May 210 325 11 24 369 300 400 16 37 438 June 230 340 12 21 385 230 350 15 30 385 July 180 275 10 20 346 200 350 18 29 362 August 190 275 11 20 354 220 350 14 25 377 September 190 270 11 19 354 250 350 20 25 400 October 200 280 12 20 362 300 400 15 35 438 November 210 300 12 24 369 500 750 16 32 800 December 220 310 13 24 377 350 450 15 35 477 This may be due to low wind speeds and calm atmospheric conditions during winter seasons, which reduces the dispersion of air pollutants into the atmosphere. Thawonkaew et al. (2016) have also reported NOx and SO 2 concentration predicted by AERMOD dispersion model in Thailand were not exceeding the annual AAQS. Thepanondh and Jitbantoung (2014) reported assimilative capacity of PM 10 , SO 2 and NO 2 in Dawai area, Thailand were found to be 0.0025, 0.0031 and 0.0075 kg ha -1 day -1 , respectively. ...
Ambient air quality monitoring (AAQM) along with assimilative capacity and Air Quality Index (AQI) studies were carried out at Indian Veterinary Research Institute (I.V.R.I), Izatnagar and Petrol Pump, Civil lines, Bareilly, India over a period of 5 year and relative humidity was varied from 35°C to 45°C (March to May) and 96 % to 97 % (December) during 2013 to 2017. Maximum wind speeds were varying from 4 to 8 m s to September and directions over the study area. Maximum ventilation coefficient (VC) values were ranging from 9000 m 2 s-1 to 12000 m ranging negative assimilative potential of 190 to 230 μg m and SPM, which were exceeding the NAAQS of 100 and 300 μg m concentrations were found to be within 90 % of guideline values and assimilative potentials were ranging from 40 winter season and 300 found to be ranging from 300
... The dispersion of VOC emissions from aboveground storage tanks are simulated by AERMOD dispersion model. This model was intensively validated and tested for its accuracy in the Eastern region of Thailand where the study area is [12,15,19,26,27,29,34]. Toxic concentrations and odor impacts of pentane at the receptors were estimated by using AERMOD. ...
Emission characteristics of volatile organic compounds (VOC) emitted from the tank farm of petroleum refinery were evaluated in this study in order to analyze for the potential impacts on health and odor nuisance problems. Estimation procedures were carried out by using the U.S.EPA TANK 4.0.9d emission model in conjunction with direct measurements of gas phase of each stored liquid within aboveground storage tanks. Results revealed that about 61.12% of total VOC emitted from the tank farm by volume were alkanes, in which pentane were richest (27.4%), followed by cyclopentane (19.22%), propene (19.02%), and isobutene (14.22%). Mostly of pentane (about 80%) were emitted from the floating roof tanks contained crude oil corresponded to the largest annual throughput of crude oil as compared with other petroleum distillates. Emission data were further analyzed for their ambient concentration using the AERMOD dispersion model in order to determine the extent and magnitude of odor and health impacts caused by pentane. Results indicated that there was no health impact from inhalation of pentane. However, predicted data were higher than the odor threshold values of pentane which indicated the possibility of odor nuisance problem in the vicinity areas of the refinery. In order to solve this problem, modification of the type of crude oil storage tanks from external floating roof to domed external floating roof could be significant success in reduction of both emissions and ambient concentrations of VOC from petroleum refinery tank farm.
... Hourly computed VC showed a similar diurnal trend in all the three seasons. Various researchers have calculated VC to understand the assimilative potential of the atmosphere (Krishna et al., 2004;Goyal et al., 2005;Rao, 2007, Thepanondh andJitbantoung, 2014;Thawonkaew et al., 2016). With an increase in solar insolation, as the day advances VC also increases reaching a maximum value during afternoon hours. ...
... January 275 325 11 22 419 320 400 12 29 454 February 225 320 13 23 381 300 350 12 28 438 March 225 300 12 22 381 250 300 13 27 400 April 200 300 11 26 362 250 310 15 28 400 May 225 300 11 25 381 300 250 15 25 438 June 225 300 13 30 381 275 325 16 26 419 July 200 300 11 24 362 260 320 12 25 408 August 200 300 11 22 362 260 330 13 27 408 September 220 320 11 23 377 280 310 14 30 423 October 220 300 12 27 377 260 350 13 29 408 November 230 310 13 32 385 300 375 12 28 438 December 265 325 12 30 412 310 400 13 31 446 January 320 375 10 21 454 320 400 14 26 454 February 240 350 12 24 392 300 350 14 27 438 March 22 300 11 25 250 350 330 13 27 477 April 200 280 12 24 362 250 375 14 30 400 May 220 300 12 20 377 250 375 15 32 400 June 220 300 14 22 377 260 400 14 26 408 July 200 300 11 23 362 300 325 11 32 438 August 200 300 13 25 362 280 350 12 28 423 September 200 300 11 24 362 275 375 15 26 419 October 180 300 12 26 346 300 380 13 30 438 November 190 270 12 24 354 310 370 16 27 446 December 220 350 12 25 377 320 400 13 25 454 January 260 340 11 21 408 350 450 15 32 477 February 250 310 12 21 400 300 450 14 35 438 March 260 320 11 22 408 350 550 12 28 550 April 250 320 12 24 400 300 450 16 37 438 May 210 325 11 24 369 300 400 16 37 438 June 230 340 12 21 385 230 350 15 30 385 July 180 275 10 20 346 200 350 18 29 362 August 190 275 11 20 354 220 350 14 25 377 September 190 270 11 19 354 250 350 20 25 400 October 200 280 12 20 362 300 400 15 35 438 November 210 300 12 24 369 500 750 16 32 800 December 220 310 13 24 377 350 450 15 35 477 This may be due to low wind speeds and calm atmospheric conditions during winter seasons, which reduces the dispersion of air pollutants into the atmosphere. Thawonkaew et al. (2016) have also reported NOx and SO 2 concentration predicted by AERMOD dispersion model in Thailand were not exceeding the annual AAQS. Thepanondh and Jitbantoung (2014) reported assimilative capacity of PM 10 , SO 2 and NO 2 in Dawai area, Thailand were found to be 0.0025, 0.0031 and 0.0075 kg ha -1 day -1 , respectively. ...
ARTICLE INFO ABSTRACT Ambient air quality monitoring (AAQM) along with assimilative capacity and Air Quality Index (AQI) studies were carried out at Indian Veterinary Research Institute (I.V.R.I), Izatnagar and Petrol Pump, Civil lines, Bareilly, India over a period of 5 year and relative humidity was varied from 35°C to 45°C (March to May) and 96 % to 97 % (December) during 2013 to 2017. Maximum wind speeds were varying from 4 to 8 m s to September and directions over the study area. Maximum ventilation coefficient (VC) values were ranging from 9000 m 2 s-1 to 12000 m ranging negative assimilative potential of 190 to 230 μg m and SPM, which were exceeding the NAAQS of 100 and 300 μg m concentrations were found to be within 90 % of guideline values and assimilative potentials were ranging from 40 winter season and 300 found to be ranging from 300
This study is aimed to compare the performance of AERMOD dispersion model by using actual and prognostic meteorological data in predicting ground level sulfur dioxide (SO 2 ) concentrations and spatial dispersion in the largest petrochemical industrial complex in Thailand. Three SO 2 monitoring stations having the highest percentage of data completeness were selected among the air quality monitoring network in the study area to serve the evaluation purpose. Emission data in this study comprised of 472 combustion stacks and 11 roads. Those emissions were assumed as constant value for each source over the simulated period. The observed air quality and meteorological data in May, 2013 were then also selected due to the occurring of hourly extreme concentration (episode) of SO 2 as well as having highest completeness of measured data. Hourly meteorological data during this period obtained from direct measurement and prognostic meteorological data were used as input independent variables in the model simulation. Evaluation of model performance was accomplished by statistical comparison between observed and modeled SO 2 concentrations. Results from statistical analysis indicated that there were no different between predicted SO 2 concentrations from using of prognostic and actual meteorological simulations. However, predicted SO 2 concentrations by AERMOD from both meteorological data provide over-estimate results when compare with those monitoring results. © 2018, Thai Society of Higher Eduation Institutes on Environment. All rights reserved.
Industrialization and increase in density of vehicle are being the major concerns of air pollution in urban areas. Focused on assimilative capacity of the atmosphere and determination of air quality index (AQI) of industrial areas are of more concern in the allocation of new industries in the existing industrial areas of towns and cities. Air quality monitoring studies were conducted from November, 2016 to June, 2017 at Hebbal industrial area, Mysuru, Karnataka, India. The wind rose plots during winter indicated the predominant wind was blowing from E (90°) and ENE (68°) and during summer wind was blowing from SW (225°) and WSW (248°) towards NE (45°) and ENE (68°). A maximum wind speed of 7.77 m/s was observed during monsoon season. The results of Ambient Air Quality Monitoring Studies (AAQMS) at Hebbal Industrial area showed, during winter season PM 10 and PM 2.5 concentrations were found to be 177.3 and 64.2 μg/m 3 , respectively at Location 1, which was more than the National Ambient Air Quality Standard (NAAQS) of 100 and 60 μg/m 3 , respectively. NOx and SO 2 concentrations at Hebbal industrial area, were found to be within NAAQS of 80 μg/m 3. The average ventilation coefficient (VC) observed to be highest during summer season (7631 m 2 /s at 16:00 h) followed by winter (5009 m 2 /s at 15:00 h) and lowest during monsoon season (4114 m 2 /s at 16:00 h). Assimilative capacity for PM 10 , PM 2.5 , NOx and SO 2 during summer and monsoon seasons were noticed to be within the permissible limits. During winter season Air Quality Index (AQI) was found to be 'moderate' at location 1 and 2, due to high pollution load, implying a 'poor' air quality during winter season. However, during summer AQI was found to be 'satisfactory' at all the locations and in monsoon season AQI at location 2 and 3 was found to be 'good' due to the occurrence of precipitation and unstable atmospheric conditions.
