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Durban Port in South Africa is the largest container port and the busiest shipping terminal in sub-Saharan Africa. Approximately 60% of the country's containerised cargo and 40% of break-bulk cargo transit through Durban. The port is near the central business district, which has a positive spin-off in terms of tourism, recreation, and accessibility to transport and other business activities. The juxtaposition of industry, the port, and the community has resulted in sustained public health implications, a relic of the apartheid era. Like most ports in Africa, Durban Port lacks proper quantification of emissions from marine mobile sources. This study is aimed at estimating atmospheric emissions from ocean-going vessels (OGVs) in and around Durban Port for a period of one year from 1 January 2018 to 31 December 2018 using a mid-tier (activity-based) approach to supplement existing understandings of emissions from local industries. Emission estimates were then inputted to the AERMOD atmospheric dispersion model to allow for a comparison between ambient concentrations and national ambient air quality standards to assess potential health impacts. The study is an advancement in understanding the impact of mobile sources, particularly shipping, on air quality and health, and offers an example for other African ports to follow. [ABSTRACT FROM AUTHOR]

Deterioration of air quality in Kuala Lumpur caused by mobile sources and traffic-related activities with interaction with climatic conditions and dispersion in the atmosphere. This study was focused on predicting the averaged 1-h concentration of particulate matter (PM10) that was emitted from private cars in Kuala Lumpur by applying the air dispersion model American Meteorological Society (AMS)/United States Environmental Protection Agency (EPA) Regulatory Model (AERMOD) in 2014. The AERMOD model indicates that private cars in Kuala Lumpur recorded 1-h concentration of PM10 below the Malaysian Ambient Air Quality Guidelines (RMAQG) in 2014. The highest concentration was 59.2 µg/m3 in 2014 and mainly concentrated around the city center and highway networks. The highest concentrations are recorded in early morning and late at night, with the highest concentrations at 4 am and 2 am and 48.68 µg/m3 and 38.42 µg/m3, respectively. Late night to early morning is identified as the dominant time interval for pollutant dispersion. The findings demonstrated that air quality had a significant impact on the 1-h concentration of PM10 emitted from private cars in Kuala Lumpur. [ABSTRACT FROM AUTHOR]

African cities grapple with urban air pollution from traffic‐related air pollutants (TRAPs). This study investigated TRAPs concentration variations at traffic intersections (TIs) in Ibadan, Nigeria. AERMOD model was employed to examine TRAPs dispersion at 25 selected TIs, considered as volume sources. Seasonal concentration distributions of six pollutants (CO, NO2, SO2, TVOCs, PM2.5, and PM10) were determined using meteorological, topographical, and pollutants' emission rates in AERMOD. Estimated peak concentrations of the studied pollutants were generally higher in the rainy season than dry season, surpassing the air quality standards set by World Health Organization (WHO) and Nigeria's National Environmental Standards and Regulations Enforcement Agency (NESREA) during the rainy and dry seasons except SO2 (24 h) concentration which did not exceed the NESREA standard. This study highlighted TIs as significant contributors to air quality degradation in both seasons studied in Ibadan and showed AERMOD's suitability for air dispersion studies. [ABSTRACT FROM AUTHOR]

In this study, the vehicular air pollution from the Nairobi Expressway corridor was estimated by simulation using AERMOD dispersion model. The Vehicle Kilometer Travelled (VKT), emission factors, and traffic data were used to estimate the pollutants from vehicular traffic.. The highest modelled 24-hour and annual concentrations of PM2.5 were 0.274 µg/m3 and 0.152 µg/m3, whereas the highest 24-hour and annual concentrations of PM10 were 0.405 µg/m3 and 0.225 µg/m3, respectively. The maximum 24-hour and annual concentrations of Total Volatile Organic Compounds (TVOCs) were 20.8 µg/m3 and 11.5 µg/m3, respectively, whereas the maximum 8-hour and annual concentrations of carbon monoxide (CO) were 319 µg/m3 and 177 µg/m3, respectively. The highest modeled 24-hour and annual concentrations of NO2 were 28.2 µg/m3 and 15.7 µg/m3, respectively. The 24-hour average measured TVOCs values showed that Nairobi expressway traffic contributed 3.5 percent to the measured TVOCs concentration value, but CO accounted for 55 percent of the measured value. According to the simulated PM2.5 and PM10 concentration values, Nairobi expressway traffic contributed 1.1 and 1.6 percent, respectively, to the 24-hour average measured concentration levels.

Modeling dispersion of aircraft emissions is challenging because aircraft are mobile sources with varying emissions rates at different elevations depending on the operating mode. Aircraft emissions during landing and take-off cycle (LTO) influence air quality in and around the airport, and depending on the number of aircraft operations and location of the airport, this influence may be significant. AERMOD (v22112) incorporates a variety of conventional source types to characterize the intended emissions source, leaving the question of which conventional source type(s) best characterizes aircraft activities across the four modes of LTO cycle, unanswered. Currently, the publicly released version of FAA's Aviation Environmental Design Tool (version 3e) models aircraft emissions as a set of AREA sources for all flight segments. A research version of AEDT allows users to model aircraft sources—both fixed wing and rotorcraft—as a series of VOLUME sources in AERMOD. However, both source treatments do not account for plume rise of aircraft jet exhaust. This paper compares AERMOD's performance in describing SO2 concentrations associated with airport sources by comparing model results from the two source options during the summer campaign of the Air Quality Source Apportionment study conducted at the Los Angeles International Airport. We conclude that both VOLUME source and AREA treatments overestimate the highest observed SO2 concentrations despite not accounting for background sources. The VOLUME source option reduces this overestimation by using a higher initial plume spread than the AREA option does, and through the inclusion of meander. Our results suggest the need to include the plume rise of jet exhaust when using AERMOD for airport air quality studies. [ABSTRACT FROM AUTHOR]

Natural gas is widely used in energy production, one of the most prominent sectors for humankind. Combustion processes inevitably produce air pollutants. The major pollutant during a combustion process is nitrogen oxide emissions. The term of nitrogen oxides primarily include nitrogen monoxide and nitrogen dioxide. These pollutants are generated regardless of the fuel content since air composition itself is the major source for these pollutants. It is possible to calculate emissions through the activity data and emission factors. Calculation of emissions is not enough for an environmental assessment. The impact of pollutants on human health relies on their concentration in the atmosphere. In order to determine their concentrations several modelling practices are developed. In this study, AERMOD used for modelling purpose of NOx emissions from a liquefied natural gas facility. It was observed that the pollutants were dispersed mostly towards south-southwest of the facility, where Marmaraereğlisi district is located. Although the pollutants transported directly to the settlement, the concentrations remained limited. During operation conditions, the highest daily NOx concentration was 1.7 µg/m³ and the highest annual concentration was 0.1 µg/m³. At maximum operating conditions, the highest daily NOx concentration was 16.2 µg/m³ and the highest annual concentration was 2.5 µg/m³. At minimum operating conditions, the highest daily NOx concentration was 1.1 µg/m³ and the highest annual concentration was 0.2 µg/m³. [ABSTRACT FROM AUTHOR]

Industrial and mining operations are classified as the primary sources of particulate matter (PM10) that pose substantial health and environmental risks, with much attention when situated in or near residential areas. The absence of a standardized approach for quantifying air pollutant fluxes in mining areas has been identified as a research gap, particularly given the increasing rate of mining extraction, especially in developing countries. This study used the AERMOD air pollutants dispersion model to analyze PM10 dispersion from the CIMERWA Cement Plant and adjacent mining sites in Rusizi District, Rwanda, throughout 2022. We developed methods to quantify PM10 horizontal and vertical fluxes and their emission rates within the framework of dispersion analysis. Results indicate high concentrations of PM10 in the vicinity of mining areas, with daily, hourly, monthly, and annual maximum mean ground-level concentrations of 158.86 µg m−3, 92.87 µg m−3, 46.15 µg m−3, and 27.65 µg m−3, respectively. Wind erosion, with a daily horizontal speed of 0.66 m s−1 and shear speed of 0.03 m s−1, contributes to a daily mean horizontal flux of 8.50 µg m−2 s−1 and vertical flux of 0.4 µg m−2 s−1 of PM10. Dispersion maps analysis highlights residents' exposure to significant levels of PM10 depicted with 28 discrete receptors in all directions. The circulation of polluted windy air masses substantially impacts regional air quality. The findings offer evidence-based recommendations for strategic mitigation measures targeted at residents, policymakers, stakeholders, and urban planners to address air quality issues arising from industrial mining activities. [ABSTRACT FROM AUTHOR]

This research thoroughly examined the emissions of primary fine particle and precursors of secondary particles (VOCs, SO2 and NOx) originating from the petroleum refinery operation. The central aim was to quantify the emission factors of fine particulate matter and analyze their spatial dispersion and source contributions, in order to evaluate their environmental impacts.The VOCs emission measurement appeared that the wastewater treatment plant unit was the most significant source of VOCs emissions, with pentane, cyclopentane, and propane being the dominant VOCs species released. The study employed the secondary organic aerosol potential (SOAP), sulfur oxidation ratio (SOR), and nitrogen oxidation ratio (NOR) methodologies to calculate the emissions of secondary PM2.5. The combustion stacks were the principal contributor to secondary PM2.5 emissions, with SO2 being the predominant secondary PM2.5 precursor species contributing to fine particulate matter, accounting for 82.5% of the total secondary PM2.5 emissions. The overall emission factor for the refinery was determined to be 0.31 g secondary PM2.5 per kg of refined crude oil. Furthermore, the analysis indicated that the combustion stacks were the primary contributors to PM2.5 concentrations at all receptor sites, accounting for 64.4%–80.8% of the total contribution, followed by the wastewater treatment unit and storage tanks. The study underscored the importance of focusing on secondary PM2.5 precursor emissions to effectively reduce emissions and environmental concentrations of PM2.5, highlighting the potential for more effective management and mitigation strategies targeting these precursors.

Air pollution in several cities in Indonesia has been increasing significantly over the years. One of the primary triggers is the rise in motor vehicle activity. This increase is due to the improving economic conditions and the decreasing prices of motor vehicles. Vehicles produce various types of pollutants. The pollutants contained in vehicle exhaust gas are carbon monoxide (CO), nitrogen oxide (NOx), sulfur (SOx) and dust (PM). One of the busiest areas for motorized vehicle activity is the Makassar Port Area, which is the largest port in Eastern Indonesia. This area experiences high vehicle activity. The emissions reviewed in this research are CO (carbon monoxide) and NOx (nitrogen oxide) in the Makassar Port Area which is located on Jalan Nusantara, Jalan Sulawesi, Jalan Siswa Army, Dr. Wahidin Jalan Sudirohusodo, Jalan Sangir, Jalan Kalimantan, Jalan Sarappo, Jalan Banda and Jalan Butung. This research utilizes historical data on traffic volume, wind speed and direction, air humidity, air temperature, air pressure, solar radiation, cloud cover and surface height. Air dispersion analysis was carried out using AERMOD software. The highest emissions obtained were 67,121 μg/m³ for CO and 9,570 μg/m³ for NOx under existing conditions and after implementing traffic management measures, the highest emissions were reduced to 45,737 μg/m³ for CO and 7,217 μg/m³ for NOx. These results conclude that traffic management can reduce air pollution. Air dispersion is not only influenced by vehicle volume but also meteorological factors. This can be seen in the dispersion results. Where, the conditions before and after traffic management showed differences in terms of the distribution of air dispersion. [ABSTRACT FROM AUTHOR]

Ammonia emissions from livestock farms are a major problem for staff and residents near these centers. Anaerobic decomposition of livestock waste and the collection and stacking of waste are the main sources of ammonia gas on livestock farms. This article examined ammonia emissions from livestock facilities near Tehran Imam Khomeini International Airport. First, field measurements were made in the summer (30°C), and collected samples were examined using the indophenol technique. AERMOD, based on Gaussian fluctuating plume theory, was constructed to model ammonia emissions. According to AERMOD, the maximum periodic‐seasonal 2‐hourly and 8‐hourly ammonia concentrations were 15.9, 229, and 63.7 OU (odor unit per unit volume)/m3, respectively. The hourly ammonia emission rate from livestock sources is also 0.0168 gm−2 h−1. AERMOD results mathematical model indicated reasonable agreement with the field measurement, which the coefficient of determination (R2), index of agreement (d), and efficiency coefficient (E) were 0.95, 0.97, and 0.91, respectively. Moreover, Statistical indices, including geometric mean bias (MG), geometric variance (VG), fraction of predictions within a factor of two of observations (FAC2), and normalized mean square error (NMSE), the root mean square error (RMSE) and mean absolute error (MAE) are also indicated by 1.36 and 0.67, respectively, were used to assess the reliability of the model results showed that AERMOD had a good agreement with the field measurement. [ABSTRACT FROM AUTHOR]

South Africa remains an uncharted realm in terms of marine mobile emissions inventory and hence the impact of pollution from ships in coastal cities remains unknown. Such a void creates uncertainties about the extent of population exposure to pollution from ships, the health impact and economic value associated with the changes in policies for the port city of Durban.This study was aimed at estimating the health and economic impact of marine mobile emissions and the health and economic benefits associated with improvement in air quality within the port city of Durban, which is under eThekwini municipality. The population exposure to particulate matter of less than 2.5 and 10 μm (PM2.5 and PM10) and sulphur dioxide (SO2) from ships calling into Durban port were estimated using the AERMOD air dispersion model. The BenMAP modelling tool was then used to estimate the health impact and economic value of changes in emission of PM2.5, PM10 and SO2 from ships visiting Durban port. The reduction in emissions from ships was due to the reduction in the sulphur content of fuel from 3.5% to 0.5% that was implemented by the International Maritime Organisation (IMO) on the 01st of January 2020.The results showed that PM2.5 and PM10 emissions reduced by 63% in 2020 compared to 2018, whilst SO2 emissions reduced by 82% over the same period. The BenMAP results indicated that 49 premature mortalities were avoided in 2020 compared to 2018 since the IMO's forced reduction in sulphur content of fuel by 85% in 2020. These avoided cases on mortality were estimated a monetary value of R228 million using the World Bank’s 2016 estimates and R683 million using the USEPA Mean VSL estimate. Such monetary values were respectively equivalent to 0.05% and 0.2% of the eThekwini's GDP, which was estimated at R468 billion in 2016.

Durban Port in South Africa is the largest container port and the busiest shipping terminal in sub-Saharan Africa. Approximately 60% of the country’s containerised cargo and 40% of break-bulk cargo transit through Durban. The port is near the central business district, which has a positive spin-off in terms of tourism, recreation, and accessibility to transport and other business activities. The juxtaposition of industry, the port, and the community has resulted in sustained public health implications, a relic of the apartheid era. Like most ports in Africa, Durban Port lacks proper quantification of emissions from marine mobile sources. This study is aimed at estimating atmospheric emissions from ocean-going vessels (OGVs) in and around Durban Port for a period of one year from 1 January 2018 to 31 December 2018 using a mid-tier (activity-based) approach to supplement existing understandings of emissions from local industries. Emission estimates were then inputted to the AERMOD atmospheric dispersion model to allow for a comparison between ambient concentrations and national ambient air quality standards to assess potential health impacts. The study is an advancement in understanding the impact of mobile sources, particularly shipping, on air quality and health, and offers an example for other African ports to follow.

This study aimed to model the dispersion of emitted SO2 from stacks and flares in one of the largest Gas Refinery Companies in the Middle East. Pollutant emission coefficients and air pollution's various sources contributions were determined based on the collected data after measuring SO2 concentrations in a fixed monitoring station (stack) and across different distances from it for a year. The SO2 release pattern was simulated, and annual pollutant concentrations in average periods of 1-hr and 24-hr were predicted using AERMOD 8.9.0. The maximum simulated ambient SO2 were 27,447 and 4592 µg/m3 in average sampling times of 1-hr and 24-hr, respectively. The hazard quotient of 95% percentile for children, teenagers, and adults due to inhalation of SO2 was more than one.The maximum concentration of SO2 in the 1-hour and 24-hour period in the study area was higher than the amount introduced by Iran's clean air standard and the WHO standard. [ABSTRACT FROM AUTHOR]

The dispersion of particulate matter (PM10) in complex topographic areas is a scientific problem that poses significant challenges to human health, environmental scientists, and policymakers. To date, misused terrain elevation data in air quality modelling studies influence the over/under predictions results. The study evaluates the AERMOD model for modelling the dispersion of particulate matter (PM10) in the complex topography of Kigali, Rwanda. It compares model outputs for four digital elevation models (DEMs), namely SRTM1 (30 m), SRTM3 (90 m), SRTM30 (900 m), and GTOPO30 (900 m). It evaluates the model performance using in-situ monitoring data and statistical metrics counting the fraction of predictions within a factor of two (FAC2), fractional bias (FB), mean bias (MB), geometric mean bias (MG), geometric variance (GV), normalized mean bias (NMB), and normalized mean squared error (NMSE). The annual averages of in-situ monitoring covering 12 months of 2021 at stations A, B, C, D, E, and F were 43.73 µg m-3, 53.64 µg m-3, 53.25 µg m-3, 42.02 µg m-3, 44.04 µg m-3, and 56.01 µg m-3, respectively. The 24-hour average for modelling results was 22 µg m-3 for SRTM1 and 23 µg m-3 for other DEMs, while the annual average was 10 µg m-3 for all DEMs. The FB metric suggests that the choice of DEM significantly impacts the accuracy of the model predictions, with SRTM3 and GTOPO30 providing the best performance at stations A and F, whereas FAC2, MB, MG, GV, NMB, and NMSE provided a meaningful model performance at stations B, C, D, and E. The study provides valuable insights into the dispersion of PM10 in complex topographic areas and can help inform air quality models and policymaking in similar regions. [ABSTRACT FROM AUTHOR]

Historically, Romania is known as a mining site of mineral substances, including gold, silver, copper, lead, zin, uranium, manganese, salt, and coal, whereby their long periods of exploitation and extraction affected human health and the environment in various ways. In Moldova Nouă southwest region of Romania, we investigated the environmental impacts of mining activities on air quality over 2021. We quantified PM10 emission rates through in situ monitoring, dispersion modelling, and horizontal and vertical fluxes. Statistical metrics, including the fraction within factor 2 (FAC2), mean bias (MB), mean gross error (MGE), normalized mean bias (NMB), normalized mean gross error (NMGE), coefficient of efficiency (COE), index of agreements (IOAs), and Taylor diagram signifying standards deviation (SD), root mean squared error (RMSE), and correlation coefficient (R), were used to evaluate the reliability of modelling results against observation. Results conclude that PM10 dispersion agrees with MB, MGE, NMB, NMGE, COE, IOA, and Taylor diagram and moderately with FAC2 metrics. PM10 hotspot was investigated in the vicinity of the tailings ponds of 115.5 µg m−3 annual mean, 563.7 µg m−3 daily mean, 63.3 µg m−2 s−1 annual horizontal flux, and 3.0 µg m−2 s−1 annual vertical flux. PM10 dispersion was identified to expand to Moldova Nouă City and nearby country Serbia. Findings concluded that a windy air mass accumulation across the overburdened dumps and ponds causes the increase of PM10 in the air, resulting in the region's pollution. Therefore, results recommend adopting a strategic mitigation measure for residents, policymakers, stakeholders, and urban planners. [ABSTRACT FROM AUTHOR]

Anaerobic process occurred in landfills emits substantial amount of methane (CH4) to the atmosphere. Methane is a strong greenhouse gas (GHG) as well as an ozone precursor therefore reduction of the emission would provide co-benefits on reduced climate forcing and improved air quality. Emission inventory for the base year of 2019 where the activity data were available was done using both Intergovernmental Panel on Climate Change (IPCC) and Landfill Gas Emissions Model (LandGEM) models. Emission results were compared with the flux rate measurement using close flux chamber at the landfill but for limited period. Two selected measures were proposed: biocover (biosolid) and pipe-flaring system and emission reductions were calculated. Dispersion analysis of base year methane emission was done using the Aermic Model (AERMOD) model with grids size of 0.5 x 0.5 km with a radius of 5 km from the landfill for maximum hourly, daily maximum, and annual average periods. The results of the model analysis showed that the locations exposed to the maximum concentration were within a radius of up to 1 km from the landfill to the eastern part of domain which is dominated by rice fields and residential area. High concentrations of methane over the residential areas potentially triggered the ozone formation as other local ozone precursors such as NOx and CO were potentially emitted. Meteorological factors, especially wind patterns, greatly affected the distribution pattern of CH4 in the study area. Simulations of two measures of bio-cover and pipe-flaring system were also done to investigate the impact on the methane air quality. Emission reduction of the pipe-flaring system yielded higher value as compared to the biocover therefore simulated methane also showed lower concentrations over the domain. In the near future, it is recommended that the pipe-flaring system to be installed in the landfill. [ABSTRACT FROM AUTHOR]

AbstractThe particulate matter (PM) emitted from multiple sources contribute to air pollution near highways, affecting, mainly the commuters on road, dwellers and shopkeepers nearby. In this study, the size distribution of the PM near a highway, obtained using real time laser aerosol spectrometer, was used to apportion the contributions from vehicular traffic and road dust. Data on vehicle count and type were obtained for the selected highway stretch. The size distribution of emission from petrol and diesel vehicles and the size distribution of re-suspended dust were obtained through independent studies. Size based distribution of PM in the study area contributed by vehicles, were modelled using AERMOD using the vehicle emissions and vehicle count data. Aggregation of the modelled concentrations of PM of various size ranges with the contribution from re-suspended dust gave size based concentration distributions closely matching with that of PM in ambient air. The size based PM analyses showed that in the study area, 47.36%, 15.74%, and 15.46% of PM2.5, were contributed by diesel vehicles, petrol vehicles and suspended dust, respectively. The corresponding contributions to PM10 were, 14.42%, 10.59% and 68.35%. The emission studies on vehicles showed diesel vehicles contributing significantly higher concentration of PM to ambient air than petrol vehicles, with condition of vehicle influencing the amount of PM emitted. The use of particle size distribution pattern for preliminary source apportionment, demonstrated in this study, has promising environmental forensic applications as it provides a cheap and fast corroborative evidence for responsibility allocation. [ABSTRACT FROM AUTHOR]

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New results are presented from wind tunnel studies performed at the United States Environmental Protection Agency (U.S. EPA), which include cases with solid roadside barriers of varying heights and cases with varying distances between the line source (roadway) and a 6-m-tall barrier. The Source-to-Barrier Distance cases include seven lanes of traffic with each lane acting as an independent source of continuous emissions along a line (i.e., line source). A mixed-wake algorithm that accounts for barrier effects within a steady-state air dispersion model was updated based on the recent wind tunnel studies. To study the effects of a solid roadside barrier, varying barrier heights and varying distances between the line source and barrier were modeled with the U.S. EPA regulatory air dispersion model AERMOD (v. 21112) using the line-source option that includes an experimental barrier option (RLINEXT). The mixed-wake algorithm reproduced the shape of the vertical concentration profiles observed in the wind tunnel data, including the uniform concentration profile from the ground vertically to a height somewhat greater than the height of the barrier. The algorithm responded appropriately to changes in barrier height and source-to-barrier distance, producing greater reductions in ground-level concentrations for taller barriers and for shorter source-to-barrier distances. Additionally, a rule of thumb that approximates the effect of a downwind barrier was formulated by converting an estimated vertical dispersion into an additional travel distance. The wind tunnel results, the update to the mixed-wake algorithm, and a comparison of the two data sets are described in this paper.

زمینه و هدف: یکی از اساسی‌ترین مشکلات آلودگی هوا این است که معمولاً مناطق وسیعی از کاربری‌های مختلف همچون شهرها و محصولات کشاورزی را در صدها کیلومتر از منشاء تولید آلاینده‌ها تحت تأثیر خود قرار داده و یا موجب اثرات تجمعی با دیگر صنایع شده است. هدف از این پژوهش کمی‌سازی غلظت آلاینده‌ها در مناطق مسکونی اطراف کاربری‌های صنعتی است.مواد و روش‌ها: در این مطالعه به‌منظور مدل‌سازی آلاینده‌های هوا از نرم‌افزار AERMOD استفاده گردید. این نرم‌افزار با استفاده از داده‌های هواشناسی، مدل رقومی ارتفاع و اطلاعات مربوط به منابع آلاینده میزان آلودگی هوای مربوط به آلاینده‌هایPM10 ، SOX، NOx و CO را در یک مکان مشخص ارزیابی و کمی سازی می‌کند.یافته‌ها: نتایج حاصل از پژوهش حاضر حاکی از این است که در این 28 نقطه جمعیتی پیرامون کاربری صنعتی مورد نظر از نظر غلظت آلاینده‌ها در هر دو حالت با و بدون غلظت زمینه، غلظت هیچ آلاینده‌ای بالاتر از حد استاندارد نبوده است؛ و مهم‌ترین آلاینده در این پژوهش NOX سالینه تشخیص داده شد که کمترین اختلاف را با حد مجاز آلودگی دارد. همچنین به دلیل نزدیکی بیشتر آلاینده‌ها به حد مجاز محیط زیست در این منطقه بارگذاری بیشتر کاربری‌های صنعتی می‌تواند باعث بروز مشکلات زیادی از لحاظ سلامتی، اقتصادی و اجتماعی شود.نتیجه‌گیری: یافته‌های این پژوهش نشان می‌دهد که به‌منظور ارزیابی و کمی‌سازی غلظت آلاینده‌های مناطق پیرامون نقاط آلاینده صنعتی علاوه بر مدل‌سازی منابع نقطه‌ای بهتر است آلودگی‌های موجود در زمینه نیز برای دقت بیشتر در راستای توسعه پایدار و درخور اینگونه مناطق بکار گرفته شوند.

Abstract The present case study considers fuel base substitution in operation of actual district heating system and in other scenario replacing of district heating system by individual heating system in each apartment building and non-residential building in selected residential zone Zvolen-Sekier, Slovakia. The impact of each heating system was assessed with focus on ambient air quality based on air dispersion modelling of NO2 and CO pollutans using the AERMOD dispersion model. To identify the exposure level on residents, the magnitude and duration of exposure to the hazard were considered according to human health risk assessment method. Results showed that the individual heating systems released significantly higher NO2 and CO concentrations directly in the residential zone compared to district heating system. The obtained results were highly variable for individual scenarios and averaged periods of pollutants concentration. Investigated heating systems scenarios showed low (

The burning of gases in torches in oil and refinery areas is a significant source of air pollution, releasing harmful pollutants into the atmosphere. This study focused on the 9th refinery of the South Pars Gas Complex in the Assaluyeh region as a case study to understand the distribution and impact of pollutants caused by combustion processes and refinery industry flares. The researchers collected air pollutant and flare emission data, weather information, and physical stack and flare information to model the distribution of pollutants using the AERMOD software. The study found that carbon monoxide emissions were higher than the standard range in autumn and winter of 2020 and summer of 2021, while NO2 emissions were lower than the standard range, and SO2 emissions were higher than the standard range across all seasons. The highest concentration of pollutants was observed in the refinery range and to the south and west of the refinery, leading to the sea, which could affect the ecosystem. The study highlights the importance of having sufficient data and database models to provide effective analysis and management strategies to control and reduce the effects of air pollutants. Overall, this study provides valuable insights into the distribution and impact of pollutants caused by combustion processes and refinery industry flares, which can inform future management strategies to mitigate the harmful effects of air pollution.

[Objetivo] Se estudió la variación de las concentraciones del sulfuro de hidrógeno (H2S) y del amoniaco (NH3) en aire, en un sistema de tratamiento de aguas residuales en Costa Rica, para conocer el posible alcance de los olores producidos, con mediciones en el lugar y un modelo de dispersión de sustancias en el aire. [Metodología] Fueron realizados 13 muestreos en periodos de 12 horas y 24 horas, entre octubre del 2016 y febrero del 2017; con las mediciones de campo, se alimentó un modelo de dispersión. Se midieron los parámetros meteorológicos: dirección y velocidad de viento, radiación global, temperatura, porcentaje de humedad relativa, precipitación y presión atmosférica. Mediante el uso de los programas Aermed View y AERMOD, se demostró la dispersión de los gases alrededor de los puntos de emisión seleccionados en la planta de tratamiento. Al Aermet View se le incorporaron los parámetros de rugosidad superficial, la proporción de Bowen y el albedo para una zona rural. Los datos recolectados se incluyeron en AERMOD. [Resultados] La concentración de los gases emitidos por la planta se encuentra por debajo de los valores de percepción de estas sustancias, 0,70 µg/m3 (< 0,50 ppb) para el H2S y 26,6 µg/m3 (< 35,5 ppb) para el NH3. Se determinó que el canal de vertido es la fuente principal de emisión. Los gases generados se dispersaron hacia las comunidades vecinas de La Carpio, María Auxiliadora y Rincón Grande, en dirección noroeste; Carvajal Castro, Rossiter Carballo, Residencial Real Santamaría y Lagunilla, en dirección noreste, y la Comisión Nacional de Emergencia, en dirección sur. Los valores de error se encontraron en un ámbito entre el 5 % y el 48 % para el H2S y entre el 8 % y el 75 % para el NH3. El índice de concordancia (IC) mostró una similitud entre los valores predichos y los valores observados, tanto para el H2S como para el NH3. [Conclusiones] Las emisiones gaseosas de H2S y NH3 provenientes del sistema de tratamiento de aguas residuales no representan un riesgo para la salud ni el medio ambiente, en las poblaciones cercanas.

In this study, we conducted a thorough investigation into the critical volatile organic compounds (VOCs), namely benzene, toluene, and xylenes (BTX), originating from the aromatic production industry. Our primary goal was to assess their spatial dispersion and source contribution, providing a comprehensive evaluation of their environmental and health impacts. The aromatic plant's average annual benzene concentrations were found to be compliant with Thailand's standard. However, xylenes did not meet the mandatory standards and emerged as the dominant species in the surrounding vicinity, with both maximum hourly and average annual concentrations exceeding the limits. Emission rate, meteorological characteristics, and topographical levels were identified as key factors affecting pollutant dispersion. The study utilized the maximum incremental reactivity (MIR) method to evaluate environmental risk assessment by calculating the ozone formation potential (OFP) of BTX. The total OFPs in the environment contributed by the aromatic plant ranged from 2.64 to 18.75 μg/m3. Xylenes emerged as the primary contributor to OFP concentrations at all receptor sites, accounting for 93–95% of the total OFP due to its high concentration and reactivity, followed by benzene and toluene. Storage tanks and wastewater treatment systems were identified as the main sources of ozone formation for benzene, toluene, and xylenes. Health risk assessment indicates an acceptable chronic hazard quotient (HQ) for each target organ system. For cancer risk, benzene slightly exceeds 10–6 at all receptors, necessitating consideration of pollutant concentrations, exposure duration, and other factors. The study emphasizes the importance of a comprehensive ambient monitoring network and updated emission inventory for effective air pollution management for the petrochemical enterprise, particularly in industrial areas.

The present case study considers fuel base substitution in operation of actual district heating system and in other scenario replacing of district heating system by individual heating system in each apartment building and non-residential building in selected residential zone Zvolen-Sekier, Slovakia. The impact of each heating system was assessed with focus on ambient air quality based on air dispersion modelling of NO2 and CO pollutans using the AERMOD dispersion model. To identify the exposure level on residents, the magnitude and duration of exposure to the hazard were considered according to human health risk assessment method. Results showed that the individual heating systems released significantly higher NO2 and CO concentrations directly in the residential zone compared to district heating system. The obtained results were highly variable for individual scenarios and averaged periods of pollutants concentration. Investigated heating systems scenarios showed low (< 1.0) hazard quotient value, however, individual heating systems would lead to adverse health effects, especially in infants and children population. [ABSTRACT FROM AUTHOR]

Emissions of greenhouse gases from industrial facilities, such as refineries, are one of the most significant environmental problems in many countries. This study aimed to assess the present status of emission sources near a gas refinery region, and the contribution of sources to air pollution was estimated by monitoring CO for a year at a fixed station. This descriptive-analytical study was conducted between January and December 2020. A simulation of CO gas distribution and pollutant concentration prediction was carried out. The results show that the maximum concentration of CO in the 1-h period was 2260 µg/m3, which corresponds to the peak concentration in spring, and in the 8-h period, it was 573 µg/m3, which corresponds to the peak concentration in winter. The studied area’s maximum pollutant concentration was also compared to national and international standards for clean air. In all four seasons, the maximum simulated CO concentrations were lower than the Iranian and EPA standards for clean air. Maximum concentrations have occurred in the southern slopes of the study area’s heights, and, due to the appropriate wind speed, maximum concentrations in the northeastern mountain peaks occurred at a more considerable distance due to the high altitude of the mountains and the lack of suitable conditions for pollutant escape. Furthermore, because of the height of smokestacks and flares from the ground and the effect of wind on the release height, the concentration of pollutants at the foot of the stacks is low and decreases gradually over a certain distance. Finally, the distribution and deposition of pollutants in the pathway of the smoke were influenced by the type of topography. [ABSTRACT FROM AUTHOR]

The adverse health effects caused by long-term exposure to high pollution volumes from industries near urban areas are a growing concern. Determining accurate distribution models of pollutants is crucial for establishing safe distances between sectors and urban regions and continuously monitoring pollutant levels. This study was conducted in Siraf City, situated in the Pars special energy zone in southern Iran, to improve the accuracy of simulation results and identify the correlation between emission models and pollutant concentrations. To achieve this goal, concentrations of seven pollutants (CO, CO 2 , NO 2 , SO 2 , O 3 , PM 2.5 , PM 10) were determined seasonally at 45 points within the study area using field sampling and numerical simulation with AERMOD software. Subsequently, the obtained results were seamlessly transferred into new domains with the primary objective of feature engineering. These engineered features were then fed into an XGBoost model for regression analysis to obtain coefficients, deriving seven equations to enhance pollutant concentration simulations' accuracy significantly. The developed equations improved the simulation accuracy for CO (12.54%), CO 2 (12.91%), NO 2 (0.94%), SO 2 (6.7%), O 3 (3.05%), PM 2.5 (12.47%), and PM 10 (4.62%). The findings demonstrate varying improved accuracy levels depending on the pollutant and simulation accuracy with well-known machine learning algorithms. The machine learning model effectively reveals the relationship between emission models and pollutant concentrations, offering valuable insights to enhance the accuracy of air pollutant emission predictions. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, Kazakhstan is one of the most polluted countries in the world. PM2.5, a major air pollutant, is six times higher than the recommended value of 5 mg/m3. The government has implemented measures to reduce air pollution, such as introducing green energy-powered buses for public transportation, but the results have not been sufficient. Therefore, it is necessary to investigate the sources of PM2.5. This study involved simulating the Combined Heat and Power Plants (CHPPs) emissions in Almaty using AERMOD and WRF for two weeks in January 2021. Two scenarios were performed: controlled and uncontrolled. The results showed that if the control mechanism of the CHPP functions at maximum efficiency, the impact of the CHPP emissions on the total emission concentration will be negligible, which is about 6% on average. However, for uncontrolled CHPPs, the emissions will contribute from 30% to 39% on average to the total PM2.5 concentration when compared with data from US Embassy monitoring stations and public air quality monitoring network, which use Pms5003 PM2.5 sensors. [ABSTRACT FROM AUTHOR]

Introduction: AERMOD model is a Gaussian model, which is widely used to model the emission of air pollutants from different sources and for distances less than 50 km from emission sources. Nowadays, petrochemical industries are one of the essential and cost-effective industries for the country. So, along with the progress of these industries, air pollution control should be considered. The purpose of this study is to model the dispersion of two pollutants, carbon monoxide, and nitrogen oxides, emitted from Maroon petrochemical stacks. Materials and Methods: In this study, the emission of carbon monoxide and nitrogen oxides from the stacks of the Maroon Petrochemical Complex was modeled using the AERMOD model in 2019. For modeling the dispersion of pollutants using AERMOD, which is the model proposed by the EPA for comprehensive studies of air pollution, the three-hour data from Bandar Mahshahr synoptic station as well as a digital elevation model with 30 m pixel size were gathered and then modeling was done in an area of 2500 square kilometers. Results: The results indicated that the average annual concentration of nitrogen dioxide and carbon monoxide pollutants was 1.44 and 1.3 mg/m3, respectively, at a distance of 900 meters from the stacks. This land area includes Maroon Petrochemical Complex and does not affect residential areas. Conclusion: The results of this study also indicated that the AERMOD model is suitable for comprehensive studies, with a focus on pollution concentration. The results of this study showed that the concentration of carbon monoxide and nitrogen dioxide pollutants is lower than the standards assigned by the Iranian Department of Environment. Maintenance programs, employee health monitoring, output control, and appropriate controls under EPA recommendations have a significant impact on controlling and reducing pollutant emissions. The result of this study also showed that the AERMOD model is highly efficient in comprehensive air pollution studies that require a detailed investigation of pollutant concentration.

The distribution and concentration of air pollutants from infectious waste incineration between 2019 and 2021 were examined in this study using an AERMOD model, including sulfur dioxide (SO2), nitrogen dioxide (NOx), and particulate matter (PM2.5). The MAIAC-AOD value at a 1 km resolution was used to develop a regression model with meteorological information for predicting PM2.5, which was then compared with the concentration from the AERMOD simulation. The following maximum 1-h, 24-h, and annual average concentrations of all pollutants were found to have occurred in 2019. The distribution of SO2 and NOx in 1 h was largest in 2020 at 1,000 m to the northwest, with concentrations of 37.68 and 93.99 μg/m3, respectively. The 24-h concentrations of SO2 and NOx were greatest in 2021 at 3.63 and 8.90 μg/m3, respectively, 720 m northeast of the stack. The annual concentrations of SO2 and NOx were highest in 2019 at 0.56 and 1.36 μg/m3, respectively. The highest annual PM2.5 concentration was 0.033 μg/m3, 730 m to the northeast in 2019. The predicted PM2.5 using MAIAC-AOD correlated with the simulated value from AERMOD, with R2 values of 0.7630, 0.7607, and 0.6504 for 2019, 2020, and 2021, respectively, which were higher closer to the stack than outside. As a result, investigations into the distribution of air pollution should benefit from the integration of air modeling and satellite information.

This study examines hilly terrain's effect on black carbon (BC) dispersion. The apportionment of distinct sources obtained by a two-component mixing model (Aethalometer: AE-33) using improved radiative transfer equations showed the dominance of traffic-derived black carbon (BCFF) emissions in the study region. The AERMOD was used to model BC emissions from moving traffic as a line source and parking lot as area source using observational and WRF-processed meteorology for the winter (January to March 2020). The model results showed that the BC levels substantially vary with local meteorological conditions, traffic volume, and composition. The hilly terrain obstructs the winds and develops a negative pressure loading to a vacuum on the other side of the hills, which promotes the accumulation of emissions, causing high BC concentrations. The pockets of higher concentration were seen at the locations where steep slopes were associated with low winds (<1 m s−1) and hill fogs. The AERMOD model, after statistical evaluation against the observational datasets, has been applied to study the reduction in BCFF concentrations due to the implementation of Indian emission norms as mitigation measures, i.e., BS-IV (equivalent to Euro 4) and BS-VI (equivalent to Euro 6). It was found that the BCFF concentrations for BS-IV and BS-VI reduced by 35% and 75%, respectively. The model was also used to study the contribution of the different vehicular categories to BC concentration. [ABSTRACT FROM AUTHOR]

Sudden air pollution accidents (explosions, fires, leaks, etc.) in chemical industry parks may result in great harm to people's lives, property, and the ecological environment. A gas tracking network can monitor hazardous gas diffusion using traceability technology combined with sensors distributed within the scope of a chemical industry park. Such systems can automatically locate the source of pollutants in a timely manner and notify relevant departments to take major hazards into their control. However, tracing the source of the leak in a large area is still a tough problem, especially within an urban area. In this paper, the diffusion of 79 potential leaking sources with consideration of different weather conditions and complex urban terrain is simulated by AERMOD. Only 61 sensors are used to monitor the gas concentration within such a large scale. A fully connected network trained with a hybrid strategy is proposed to trace the leaking source effectively and robustly. Our proposed model reaches a final classification accuracy of 99.14%. [ABSTRACT FROM AUTHOR]

Mining is one of the principal economic activities in Mexico, which in addition to bringing benefits to the population, causes health and environmental problems. This activity produces a lot of wastes, but the main one is tailings. In Mexico, these wastes are disposed of in the open air, and there is no control over them, so the particles of these wastes are dispersed by wind currents to the surrounding population. In this research, tailings were characterized, finding in them particles smaller than 100 microns; in this way, tailings can enter into the respiratory system and hence can cause diseases. Furthermore, it is important to identify the toxic components. The present work does not have previous research in Mexico, and it shows a qualitative characterization of the tailings from an active mine using different analytical techniques. In addition to the data obtained from the characterization of the tailings, as well as the concentration of the toxic elements found, which were Pb and As, a dispersal model was generated and used to estimate the concentration of particles in the wind generated at the studied area. The air quality model used in this research is AERMOD, where it uses emission factors and available databases provided by Environmental Protection Agency (USEPA); Moreover, the model was coupled with meteorological information from the latest generation WRF model. The modeling results estimated that the dispersion of particles from the tailings dam can contribute up to 10.15 µg/m3 of PM10 to the air quality of the site, which, according to the characterization of the samples obtained, could be dangerous for human health and can be estimated up to a concentration of 0.04 µg/m3 of Pb and 10.90 ng/m3 of As. It is very important to make this kind of research to know the risk which people around this disposal sites are exposed to. [ABSTRACT FROM AUTHOR]

Petrochemical companies especially those with olefin plant have a considerable negative effect on air quality, environment and people health. Therefore, measurement of emission rates of pollutants and their dispersion is required to determine policies and restrictions on pollutant emissions. In this investigation, the concentrations of some perilous pollutants such as CO, NOx and SO2 from seven active stacks of an olefin plant for all seasons of a year were measured. This olefin plant is located in one of the most important industrial centers in south of Iran called Petrochemical Special Economic Zone (PSEZ). Moreover, in all residential areas near the PSEZ, AERMOD model and WRF model were utilized for dispersion modeling and predicating the concentration of pollutants released in atmosphere from the stacks. Measurement results showed that the highest amount of pollutant emitted by the stack is NOx, and as a result, the emission rates of NOx in different seasons are significantly higher than the emission rates of CO and SO2. Moreover, according to the dispersion modeling and meteorological data, the direction of movement and development of concentration contours are toward the southwest and against the direction of residential areas. This is due to the prevailing wind directions in all seasons. Finally, in comparison with WHO (World Health Organization) air quality guideline values, the concentration of CO and SO2 was higher than the guideline values in Sarbandar in summer, fall and winter seasons as a consequence of short distance between this residential area and the sources of pollutants emission. Also, the concentration of NOx is considerably higher than the guideline values in the all residential areas during a year. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide a preliminary evaluation of the impact caused by NO2 emissions from a heating plant on the local air quality. To model the spatial distribution of NO2 emissions from the heating plant, the AERMOD model was employed. The evaluation was conducted by analyzing and contrasting the simulated NO2 concentration from the heating plant with the overall NO2 concentration recorded at the nearest monitoring station over the entire heating season. The study focused on the Zemun heating plant situated in the Zemun municipality of Belgrade. Operating at full capacity and running continuously for 24 hours, the heating plant's maximum 24-hour average modeled NO2 concentration was roughly 10% of the total NO2 concentration measured at Zemun's air monitoring station. This finding suggests that the heating plant does not constitute the primary source of NO2 pollution in the vicinity. In order to gauge the responsiveness of the proposed method for initial impact assessment, the model also simulated variations in meteorological and emission factors for the five highest 24-hour concentrations originating from the heating plant. The sensitivity analysis uncovered that reduced wind speed could result in a 60% upsurge in NO2 concentrations, and a reduction in NO2 emissions could bring about a 50% decrease in concentrations originating from the heating plant. [ABSTRACT FROM AUTHOR]

Obtaining accurate emissions of methane (CH_4), one of the most important non-carbon-dioxide greenhouse gases, is the basis for formulating and validating emission reduction policies. In terms of shortcomings from the "bottom-up" approach, this study combined the vehicle-based monitoring and the AERMOD atmospheric dispersion modeling system to derive the emission rates and emission factors of main CH_4 sources in one demonstration coal mine in Jincheng city, Shanxi province. After systematically considering the topography, meteorological conditions, and infrastructure distribution of the coal mine, both the mobile and downwind stationary monitoring alternatives were adopted, using a platform equipped with a high-precision greenhouse gas analyzer. Results showed that the simulated CH_4 emission rate of a single ventilation shaft under non-production condition (763 kg/h) was about 15.9% lower than the data provided by the enterprise in production. If ignoring the fugitive emissions, the derived CH_4 emission factor of the coal mine was 15.09 m^3/t, which was 13.8% smaller than that in " bottom-up" inventory, indicating that the working conditions of the coal mine played a large role in CH_4 emissions. One ventilation shaft and two vent stacks in the gas gathering station were the main point sources, and six coal silos were the fugitive sources, the emission factors of which were 8.6 m^3/t( 43%), 6.49 m^3/t (33%) and 4.87 m^3/t (24%), respectively. The traditional "bottom-up" accounting without consideration of fugitive emissions, resulted in a nearly 24% under estimation of CH_4 emissions even under non-production conditions, which could be compensated by the methane quantification method based on vehicle-based monitoring.

Background: The establishment of automotive industries in urban areas can affect the air quality of cities due to the variety of pollutants emitted from them. The aim of this study is to investigate the simultaneous effects of the Saipa automobile industry and two surrounding highways on air quality. Methods: In this study, meteorological data suitable for modeling the distribution of pollutants have been prepared from the Mehrabad meteorological station, and the modeling of the distribution of nitrogen dioxide and carbon monoxide gaseous pollutants released from automobile factories and linear sources was done by AERMOD software, and the accuracy of the results It was confirmed by field measurements of NO2 pollution by passive adsorbents. Results: The validation results show the acceptable performance of the model. The highest annual nitrogen dioxide concentration of 1209 parts per billion can be seen at the coordinates of 515326.80 and 3953279.17 meters inside the Lashkari highway, which is about 23 times the NAAQS standard, and the highest concentration of CO, 12.6 parts per million, can be seen at the coordinates of 515326.80 and 3953279.17 meters inside the Lashkari highway, which is about 1.4 times the NAAQS standard. Conclusion: The exposure of people passing by on the roads to concentrations higher than the NAAQS standards of nitrogen dioxide and carbon monoxide shows the need for the government and authorities to take action in relation to highways and traffic laws.

One of the main reasons for air pollution is industrial plants releasing huge amounts of air pollutants in the form of gas emissions. The different chemical pollutants and their corresponding levels present in these emissions, and their proximity to the industrial source, have serious effects on the nearby ecosystems. Some of the industrial nuisances include noise, smoke, dirt, dust, odor and noxious gases, which have to be minimized (if possible, eliminated), especially if the location is desired to be used as a community site. When choosing locations at which to build either new industrial plants or new community sites, software can be used to assess both the short-term and long-term concentration profiles of the various detrimental air pollutants. In this study, the AERMOD model was used to find an optimal location to build a new plant in Toledo, Ohio, USA. Simulations were performed to study the pollutant emissions and their dispersion patterns for four different geographic locations situated away from an existing plant in this region. The AERMOD model, along with the IRAP-h View model, which is approved by the US Environmental Protection Agency (EPA), has been successfully used to assess the fate and transport of pollutants from the proposed new industrial plants. The hazard quotients from the analysis of the results for these four different geographic locations were assessed. The highest total non-cancer hazard indices of 18.7 and 13.2 were obtained for fisher adult and fisher child, respectively, in one of the four locations. The acute inhalation quotient risk was less than the target hazard index of 0.25 for all the four locations. With respect to the concentrations of several chemicals of potential concern (COPC), such as soil, produce, beef, chicken, milk and pork, the fourth location (farthest east) recorded the minimum range values compared to the other three locations.

A significant part of the population in Serbia relies on central heating systems during the winter months, with around fifty heating plants in operation across the country. Common fuels used in these plants primarily include fossil fuels such as fuel oil and natural gas. Combustion of some of these fuels leads to significant emissions of air pollutants. This study evaluates the impact on the air quality of the two heating boilers at the Valjevo city, Serbia, heating plant. Air emissions were measured separately for two heating boilers in the facility using standard reference methodology. The AERMOD air dispersion model was used to estimate the dispersion of various pollutants. A combination of topographical and meteorological data was used to set up a receptor grid exposed to air pollution within a 10 km radius around the heating plant. It was found that the resulting distribution and concentration gradient of pollutant gases and particles were less inclined towards the city center and instead spread eastwards into the surrounding villages. The health risk from the fuel oil boiler was shown to be significantly higher than that caused by the natural gas-fuelled boiler. Nevertheless, the calculated carcinogenic and non-carcinogenic health risks were within acceptable limits. However, further research is required to adequately assess the cumulative health risk generated by other surrounding emitters. [ABSTRACT FROM AUTHOR]