Source Complex Short Term Model Research Articles

Assessment of PM2.5 Chemical Composition and Air Quality Monitoring: Implications of Air Pollutants Emissions from a Night Market in Kaohsiung City, Taiwan

ABSTRACT There are around 300 night markets in Taiwan, and they have been drawing an increasing number of tourists in recent years. As a result, public awareness over air quality in the night markets has grown tremendously. In response to this, a specific night market in Kaohsiung City was chosen for this study in order to characterize the existing air quality in and around the night markets. In this present study, we employed an Industrial Source Complex Short-Term (ISCST3) air quality model for the simulation of PM2.5 diffusions. The model as a technique can simulate the pollutants emissions, diffusions, transportation, and pollution sources in specific areas and subsequently evaluate the influence between the source and the receiver. Therefore, we compared pollutants emissions data from several air quality monitoring stations with our sampling data of three different sampling sites in Kaohsiung City. The findings of this study showed that the average concentration of PM2.5 was in the range of 29–61 µg m–3 during opening hours of the night market, whereas the average concentration of PM2.5 range was between 22–38 µg m–3 before the night market opening hours. The concentration of metallic elements (ME) (Mg, Na, Cr, Mn, Fe, Cu, Al, Ba, Cd, Pb and Ca) was determined with the support of Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). During the night market opening hours, the result disclosed that the ME concentrations in PM2.5 was in an increasing order as follows: Na > Fe > Al > Ca. With respect to the concentration of carbonaceous species, our results showed that the highest total carbon (TC) concentration was found to be 6.52 µg m–3 during the downwind sampling interval. The highest elemental carbon (EC) and organic carbon (OC) concentration were found to be 6.53 µg m–3 and 2.70 µg m–3 of the PM2.5 concentration, respectively. This study’s findings have significant consequences for Taiwan policymakers and urban planners, particularly those responsible for coordinating environmental protection and economic development in cities. Therefore, policy actions to abate urban air pollution can be attained on diverse governing echelons, resulting in synergistic effects such as a reduction in climate change impacts.

Emission load distribution and prediction of NO2 and PM10 using ISCST3 and CALINE4 line source modeling

Mysore district is one of the tourist destinations in Karnataka, India, with a population of about 10.25 lakhs. In all category of vehicles, 2-wheelers (2W) constitute more than 50 %. Vehicular count studies showed a maximum number of vehicles during peak traffic hours (7:00-11:00 hrs. and 18:00-22:00 hrs.). The maximum traffic count was observed for 2W (51-56%) followed by 4W (19-28%), LDV (light-duty vehicle), HDV (heavy-duty vehicle) and 3W. However, the percentage contribution of NO 2 and PM 10 emission factor showed, a maximum of HDV (75-89%) followed by LDV, 2W, 3W, and 4W. Emission loads for CO and NO 2 pollutants were found to be high at link 1, 2, 5, 6, 12 and 13 due to heavy traffic densities. However, emission loads for PM 10 , PM 2.5, and SO 2 were notices to be very less in all the links. During winter season Industrial source complex short-term model 3 (ISCST3) and California line source model 4 (CALINE4) predicted NO 2 concentrations were found to be ranging from 9–25 μg/m 3 and 10–40 μg/m 3 , respectively. However, PM 10 concentrations were found to be 18–43 μg/m 3 . The maximum concentration was found near to Receptors (R1-3), which may be due to maximum vehicular density and downwind to the pollution sources. NO 2 and PM 10 predictions made by ISCST3 and CALINE4 model showed a significant variation. It was also observed that CALINE4 model was not applicable to predict pollutant with lower concentrations.

Factors Associated with Persistent Lower Respiratory Symptoms or Asthma among Residents Exposed to a Sulphur Stockpile Fire Incident

Introduction: Residents of Macassar, South Africa, were exposed to sulphur dioxide vapours (SO2) caused by an ignited sulphur stockpile, which produced peak hourly SO2 levels of 20–200 ppm. The aim of this study was to assess the risk factors associated with persistent lower respiratory symptoms (LRS) or asthma six years after acute exposure to high SO2 levels. Methods: A case-control study of residents that presented for a health evaluation six years after the incident was conducted. Survey instruments included a questionnaire, clinical examination and medical record review by an expert panel. A “case” was defined as a resident with persistent LRS/asthma. The Industrial Source Complex Short Term Model (ISCST 3) was used to predict time-averaged hourly SO2 levels. Results: A previous history of pulmonary tuberculosis (PTB) was associated with persistent LRS/asthma (ORudj: 3.49, CI: 1.46–8.35). Cases were more likely to report chest tightness (ORudj: 9.93; CI: 5.15–19.11) at the time of the incident. Peak exposure at hour 15 was associated with persistent LRS/asthma (ORadj: 1.04; CI: 1.01–1.07). Conclusion: LRS/asthma persisted in some individuals six years after acute SO2 exposure. Aside from peak exposures, initial chest tightness and a previous history of PTB were the strong predictors of persistent LRS/asthma.

Analysis of air pollution emission patterns in the vicinity of oil refineries in Kuwait

This study focuses on the assessment and analysis of air pollution in the vicinity of oilrefineries in Kuwait. The emission inventory obtained for oil refineries was used topredict pollutant concentrations and distributions using the AERMOD software (V5.1),Industrial Source Complex Short-Term model (ISCST3). Pollutant concentrations andmeteorological parameters were measured at 5-minutes intervals for 24 hours/day inthree regions. The first region was in the Fahaheel residential area (north of the oilrefineries), the second region was in the Umm Alhyman residential area (south of theoil refineries), and the third region was in the Ahmadi residential area (northwest ofthe oil refineries). The measured concentrations of all pollutants are higher than thepredicted concentrations generated by the refinery emissions, which is attributed tothe effect of other pollution sources, e.g., traffic, power plants, and oil fields. Analysisshowed that the strongest contribution comes from the oil fields, followed by trafficor oil refineries.

A Feasible Approach to Quantify Fugitive VOCs from Petrochemical Processes by Integrating Open-Path Fourier Transform Infrared Spectrometry Measurements and Industrial Source Complex (ISC) Dispersion Model

Fugitive emissions are one of the largest sources of volatile organic compounds (VOCs) from petrochemical and chemical plants. However, how to quantify the total fugitive VOC emissions from numerous and mostly inaccessible sources is a time consuming and costly task. This study presents a feasible approach to quantify the fugitive VOC emissions by integrating OPFTIR measurements and the well-developed Industrial Source Complex Short Term Model (ISCST3). A mobile OPFTIR system was set up for 190 hours in the downwind location of a 1,3-butadiene manufacturing process, which has unidentified fugitive sources and should be responsible for the elevated atmospheric 1,3-butadiene concentrations. Wind speeds and directions were found to be the most important factors in the dispersion of the emissions. Therefore, when using trial and error to predict the fugitive 1,3-butadiene emission rates, we divided the field measurement data based on the wind directions and excluded that obtained during lower wind speeds. Then the correlation coefficients between the field data (from the mobile OPFTIR system) and the modeling data (from the ISCST3) were found to be up to 0.529, and the slope of the correlation equation was close to unity. Therefore, integrating the OPFTIR measurement and ISCST3 is a feasible approach to predict the amount of fugitive VOC emissions.

Simulation of Environmental Pollution Due to SO2 Dispersion

Other than volcanic activities, sulfur dioxide emission is common in various coal, oil, and gas industries. Sulfur dioxide has a great potential for long-range transport (beyond 500–1,000 km) and can create regional acid rains. Efficient modeling of SO2 dispersion process plays a crucial role in strategic environmental planning. A two-dimensional model is developed in this work to predict the SO2 emission under different meteorological conditions. Finite difference and orthogonal collocation techniques are used to solve the model partial differential equations. The simulation results show proper agreement with the results obtained from a commercial software (ISC-AERMOD) using the Industrial Source Complex Short Term model.

Long-term monitoring and modeling of polychlorinated dibenzo-p-dioxins and dibenzofurans from municipal solid waste incinerators and surrounding area in northern Taiwan.

Municipal solid waste incinerators (MSWIs) have long been the major contributors of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) to ambient air in Taiwan. After stringent MSWI emission standards were introduced in 2001, the long-term continuous monitoring of flue gas and ambient air quality became necessary to ensure the effectiveness of the related control strategies. Three MSWIs and the surrounding ambient air were investigated in the current study for PCDD/F characteristics during 2006 to 2011. The average concentrations in the flue gas ranged from 0.008 ~ 0.0488 ng I-TEQ/Nm(3), which is much less than the emission standard in Taiwan (0.1 ng I-TEQ/Nm(3)) (I-TEQ is the abbreviation of International Toxic Equivalent). This led to extremely low levels in the ambient air, 0.0255 pg I-TEQ/Nm(3), much less than the levels seen in most urban areas around the world. Additionally, the results obtained using the Industrial Source Complex Short-Term Dispersion Model (ISCST3) indicate that the PCDD/F contributions from the three MSWIs to the ambient air were only in the range from 0.164 ~ 0.723 %. Principal component analysis (PCA) showed that the PCDD/Fs in the air samples had very similar characteristics to those from mobile sources. The results thus show that stringent regulations have been an effective control strategy, especially for urban areas, such as Taipei City.

Emissions of polychlorinated-p-dibenzo dioxin, dibenzofurans (PCDD/Fs) and polybrominated diphenyl ethers (PBDEs) from rice straw biomass burning

Biomass burning in Asia has been widely studied owing to its adverse effects on visibility, human health, and global climate. However the impact of rice straw burning on polychlorinated dibenzo-p-dioxin and dibenzofurans (PCDD/Fs) and polybrominated diphenyl ethers (PBDEs) concentrations is not known. In this study concentrations of these pollutants were measured at a farm site and two nearby sites during rice straw (open) burning and non-burning periods. During non-burning periods atmospheric PCDD/F and PBDE concentrations ranged from 0.0263 to 0.0329 pg I-TEQ/Nm3 and 43.5 to 58.3 pg/Nm3 respectively, and were similar at all of the sites. During rice straw burning periods PCDD/F and PBDE concentrations measured near the combustion (farm) site increased dramatically by six to twenty times. The strong correlation between the natural logarithm of PBDE and PCDD/F concentrations (r = 0.949, p < 0.01) at each site indicates that the elevated PCDD/Fs and PBDEs were due to emissions from the rice straw burning. The calculated emission factors, determined using the burned carbon method and the Industrial Source Complex Short-Term Dispersion Model (ISCST3), ranged from 12.6 to 14.5 ng TEQ/kg Cburned and 11.7 to 14.5 μg/kg Cburned, for PCDD/Fs and PBDEs respectively. The PBDE emission factors were at least 38 times higher than those of PCDD/Fs, revealing that rice straw burning is an important PBDE emission source.

Pesticides Exposure Assessment of Kettleman City Using the Industrial Source Complex Short-Term Model Version 3

Kettleman City, California, reported a higher than expected number of birth defect cases between 2007 and 2010, raising the concern of community and government agencies. A pesticide exposure evaluation was conducted as part of a complete assessment of community chemical exposure. Nineteen pesticides that potentially cause birth defects were investigated. The Industrial Source Complex Short-Term Model Version 3 (ISCST3) was used to estimate off-site air concentrations associated with pesticide applications within 8 km of the community from late 2006 to 2009. The health screening levels were designed to indicate potential health effects and used for preliminary health evaluations of estimated air concentrations. A tiered approach was conducted. The first tier modeled simple, hypothetical worst-case situations for each of 19 pesticides. The second tier modeled specific applications of the pesticides with estimated concentrations exceeding health screening levels in the first tier. The pesticide use report database of the California Department of Pesticide Regulation provided application information. Weather input data were summarized from the measurements of a local weather station in the California Irrigation Management Information System. The ISCST3 modeling results showed that during the target period, only two application days of one pesticide (methyl isothiocyanate) produced air concentration estimates above the health screening level for developmental effects at the boundary of Kettleman City. These results suggest that the likelihood of birth defects caused by pesticide exposure was low.

Uncertainty and sensitive analysis of environmental model for risk assessments: An industrial case study

The objectives of this paper are the application of uncertainty and sensitivity analysis methods in atmospheric dispersion modeling to study the prediction of the dispersion of pollutants in the atmosphere. The Gaussian Plume Model is used to study the impact of meteorology on the dispersion of the emissions from an industrial source complex. The determination of ground-level concentration and maximum ground-level concentration is useful for the prediction of violations of air quality regulations. The Industrial Source Complex Short-Term (ISCST-3) air pollution model was adopted to predict the ground-level concentration of sulfur dioxide (SO2) emitted by a power plant located in an industrial region site in Spain. Quantitative uncertainty analysis has become a common component of risk assessments. Uncertainties were defined a priori for each of the following variables: wind speed, wind direction, and pollutant emission rate. In order to obtain information about the uncertainty of computer code results, a number of code runs was performed using the nonparametric tolerance limits method. The Monte Carlo method was used to propagate uncertainty across codes. The Spearman rank correlation coefficient was used as a sensitivity measure.

Development of a Livestock Odor Dispersion Model: Part II. Evaluation and Validation

ABSTRACT A livestock odor dispersion model (LODM) was developed to predict odor concentration and odor frequency using routine hourly meteorological data input. The odor concentrations predicted by the LODM were compared with the results obtained from other commercial models (Industrial Source Complex Short-Term model, version 3, CALPUFF) to evaluate its appropriateness. Two sets of field odor plume measurement data were used to validate the model. The model-predicted mean odor concentrations and odor frequencies were compared with those measured. Results show that this model has good performance for predicting odor concentrations and odor frequencies. IMPLICATIONS This study provides an evaluation and validation for a newly developed LODM. Two sets of field odor plume measurement data were used. Agreement fractional bias values were applied to compare modeled predicted odor intensities and measured intensities. Fac2 (the percentage of the predictions within a factor of 2 of the observed values) and defined Ran0.1 and Ran0.2 values (the percentage of the predictions within a range of ±0.2 and ±0.1, respectively, of the observed values) were used to validate the model performance of predicting odor frequency.

Air quality modelling study to analyse the impact of the World Bank emission guidelines for thermal power plants in Delhi

Recent strategies for air pollution control in Delhi have largely neglected the emission reduction measures from thermal power plants (TPPs), which are the second most polluting sources. The present study investigates how the ambient air quality of Delhi would improve if the World Bank emission guidelines (WBEG) for the TPPs were to be implemented. To accomplish this, a comprehensive inventory of point, area, and line sources was conducted in the selected study area, primarily aiming to estimate the sectoral emission contributions to ambient air quality. The Industrial Source Complex Short–Term Model, Version 3 (ISCST3) was used to predict the ambient concentrations of total suspended particulates (TSP), sulphur dioxide (SO2), and nitrogen dioxide (NO2) at seven monitoring sites (receptor locations) operated by the Central Pollution Control Board (CPCB) for the period from July 2004 to June 2005. The ISCST3 model predictions for TSP and NO2 were satisfactory at all receptor locations. However, for SO2, the model predictions were satisfactory at only two receptor locations. The vehicles contributed 58% of the total ambient air pollution, followed by TPPs contributing 30%. The study estimates that adoption of WBEG may reduce the ambient air pollution due to TPPs emissions by 56% to 82%, bringing it within the National Ambient Air Quality Standards (NAAQS) set for industrial areas in India, except at one location where TPP’s contribution to ambient air pollution is negligible compared to vehicular emissions.

GIS-Based Emission Inventory, Dispersion Modeling, and Assessment for Source Contributions of Particulate Matter in an Urban Environment

The Industrial Source Complex Short Term (ISCST3) model was used to discern the sources responsible for high PM10 levels in Kanpur City, a typical urban area in the Ganga basin, India. A systematic geographic information system-based emission inventory was developed for PM10 in each of 85 grids of 2 × 2 km. The total emission of PM10 was estimated at 11 t day−1 with an overall breakup as follows: (a) industrial point sources, 2.9 t day−1 (26%); (b) vehicles, 2.3 t day−1 (21%); (c) domestic fuel burning, 2.1 t day−1 (19%); (d) paved and unpaved road dust, 1.6 t day−1 (15%); and the rest as other sources. To validate the ISCST3 model and to assess air-quality status, sampling was done in summer and winter at seven sampling sites for over 85 days; PM10 levels were very high (89–632 μg m−3). The results show that the model-predicted concentrations are in good agreement with observed values, and the model performance was found satisfactory. The validated model was run for each source on each day of sampling. The overall source contribution to ambient air pollution was as follows: vehicular traffic (16%), domestic fuel uses (16%), paved and unpaved road dust (14%), and industries (7%). Interestingly, the largest point source (coal-based power plant) did not contribute significantly to ambient air pollution. The reason might be due to release of pollutant at high stack height. The ISCST3 model was shown to produce source apportionment results like receptor modeling that could generate source apportionment results at any desired time and space resolution.

Impact Assessment of Respirable Suspended Particulate Matter from Diesel Generator Sets Used for Pumping Station

Prediction of respirable suspended particulate matter impacts of diesel generator sets used for pumping station has been made using meteorological data, information on stack characteristics and emission rate, baseline ambient particulate matter and Industrial Source Complex Short Term (ISCST-3) model. It is observed that particulate matter emission from pumping station-S workplace diesel generator sets ranged from 2.4 to 436.5 mg Nm⁻³ and while at pumping station-C, it ranged from 23.2 to 186.5 mg Nm⁻³. The predicted and ambient respirable suspended particulate matter concentrations are below the national air quality standard for respirable suspended particulate matter in a mixed industrial area. Metals contents in respirable suspended particulate matter indicate the origin of crustal and mobile sources. Therefore, the impact of diesel generator sets used for pumping of crude oil on local air quality would be acceptable.

Application of California Puff (CALPUFF) model: a case study for Oman

Oil Refineries surrounding the gulf are the largest source of environmental pollution in the region. Air dispersion models are powerful tools for assessing the consequences of environmental air pollutant concentrations. This study was carried out to investigate the transport and dispersion patterns of SO2 originating from Mina Al-Fahal refinery, in the Sultanate Oman by employing California Puff (CALPUFF) dispersion modeling system. The major goal of this study is to make a comparison of the results produced by this modeling system with a previous study which was conducted for the same area using Industrial Source Complex Short Term (ISCST) model. In order to obtain the meteorological fields of the study area the CALPUFF modeling system was coupled with Weather Research and Forecasting (WRF), a prognostic meteorological model. The results indicated that the performance of the CALPUFF was better than that of ISCST; however, a difference in magnitudes of predicted and measured concentrations of SO2 was found. This difference can be reduced using high-resolution terrain elevation data, site-specific observational meteorological data and buoy data. The complex geography and variable wind regimes played an important role in distribution of SO2 in and around the refinery. The land–sea interaction also influenced the predicted results.

Integrated assessment of brick kiln emission impacts on air quality

This paper presents monitoring results of daily brick kiln stack emission and the derived emission factors. Emission of individual air pollutant varied significantly during a firing batch (7 days) and between kilns. Average emission factors per 1,000 bricks were 6.35-12.3 kg of CO, 0.52-5.9 kg of SO(2) and 0.64-1.4 kg of particulate matter (PM). PM emission size distribution in the stack plume was determined using a modified cascade impactor. Obtained emission factors and PM size distribution data were used in simulation study using the Industrial Source Complex Short-Term (ISCST3) dispersion model. The model performance was successfully evaluated for the local conditions using the simultaneous ambient monitoring data in 2006 and 2007. SO(2) was the most critical pollutant, exceeding the hourly National Ambient Air Quality Standards over 63 km(2) out of the 100-km(2) modelled domain in the base case. Impacts of different emission scenarios on the ambient air quality (SO(2), PM, CO, PM dry deposition flux) were assessed.

Comparison between the Application Results of NNM and a GIS-based Decision Support System for Prediction of Ground Level SO2Concentration in a Coastal Area

A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of selecting a dispersion model or a modeling scheme, originally devised by Park and Seok,(l) was developed using our GIS-based DSS. The performances of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level SO₂ (1 hr) concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were found to improve the accuracy of predicted ground level SO₂ concentration significantly, compared to the fumigation models. The GIS-based DSS may serve as a useful tool for selecting the best prediction model, even for complex terrains.

Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait

This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network‐based scheme is suggested and applied to site‐specific short‐ and medium‐term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.

Modeling of nitrogen oxides (NOx) concentrations resulting from ships at berth

Oxides of nitrogen (NOx) emissions from ships (marine vessels) contribute to poor air quality that negatively impacts public health and communities in coastal areas and far inland. These emissions often excessively harm human health, environment, wildlife habituates, and quality of life of communities and indigenous of people who live near ports. This study was conducted to assess the impact of NOx emissions origination from ships at berth on a nearby community. It was undertaken at Said Bin Sultan Naval base in Wullayat Al-Mussana (Sultanate of Oman) during the year 2005. The Industrial Source Complex Short Term (ISCST) model was adopted to determine the dispersion of NOx into port and beyond into surrounding urban areas. The hourly and monthly contours (isopleths) of NOx concentrations in and around the port were plotted. The results were analyzed to determine the affected area and the level of NOx concentrations. The highest concentration points in the studied area were also identified. The isopleths of NOx indicated that most shipping emissions of NOx occur at the port can be transported over land. The output results can help to derive advice of recommendations ships operators and environmentalists to take the correct decision to prevent workers and surrounded environment from pollution.

Assessment of polychlorinated dibenzo-p-dioxins and dibenzofurans contribution from different media to surrounding duck farms

Since the "Toxic Egg Event" broke out in central Taiwan, the possible sources of the high content of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in eggs have been a serious concern. In this study, the PCDD/F contents in different media (feed, soil and ambient air) were measured. Evaluation of the impact from electric arc furnace dust treatment plant (abbreviated as EAFDT plant), which is site-specific to the "Toxic Egg Event", on the duck total-PCDD/F daily intake was conducted by both Industrial Source Complex Short Term model (ISCST) and dry and wet deposition models. After different scenario simulations, the worst case was at farm A and at 200 g feed and 5 g soil for duck intake, and the highest PCDD/F contributions from the feed, original soil and stack flue gas were 44.92, 47.81, and 6.58%, respectively. Considering different uncertainty factors, such as the flow rate variation of stack flue gas and errors from modelling and measurement, the PCDD/F contribution fraction from the stack flue gas of EAFDT plant may increase up to twice as that for the worst case (6.58%) and become 13.2%, which was still much lower than that from the total contribution fraction (86.8%) of both feed and original soil. Fly ashes contained purposely in duck feed by the farmers was a potential major source for the duck daily intake. While the impact from EAFDT plant has been proven very minor, the PCDD/F content in the feed and soil, which was contaminated by illegal fly ash landfills, requires more attention.