scholarly journals Comparison of Application of AERMOD and ISCST3 Models for Simulating the Dispersion of Emitted Pollutant from the Stack of an Industrial Plant in Different Time Scales

2019 ◽  
Author(s):  
Mehrshad Bajoghli
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Ground Level ◽  
Atmospheric Pollutants ◽  
Industrial Plant ◽  
Simultaneous Application ◽  
Modeling Process ◽  
Total Suspended Particles ◽  
Decision Methods ◽  
Simulated Concentration

Background: One of the main parts of air quality management is known as modeling of atmospheric pollutants. In this regards, simultaneous application of several models in a project and comparing the results obtained from these models could have been a considerable contribution to air quality managers for taking a more efficient decision. Methods: In this study, the stack of an industrial plant in the southwest of Isfahan was selected as the emission source and the total suspended particles emitted from this stack was simulated by applying AERMOD and ISCST3 view models (version 8.2). In this vein, the modeling process was conducted using MM5 meteorological data in a 50 50 km extent with 2000 m network distance for each of the models in 1-h, 24-h term averages (short term averages) and monthly and annual periods (long term averages) at ground level concentrations (GLC). Results: Results indicated that the highest simulated concentration for both models occurred in a 2000 meters’ distance in the east of the stack. Moreover, the highest simulated concentration applying AERMOD was lower than that of applying ISCST3 in all term averages which is due to existing differences between applied algorithms in these two models. Conclusion: Consequently, applying AERMOD due to the use of more advanced and up-to-date algorithms have priority over ISCST3 model. Applying ISCST3 can also be useful for small projects that require less input data compared to the AERMOD.

Calculated Ground Level Concentration of Pollutants From Gas Turbine Using an Air Quality Display Model Computer Program

1974 ◽  
Author(s):  
N. R. Dibelius ◽  
George Touchton ◽  
Thomas Kane
Keyword(s):  
Air Quality ◽  
Computer Program ◽  
Gas Turbine ◽  
Meteorological Data ◽  
Ground Level ◽  
The United States ◽  
Combined Cycle ◽  
Ground Level Concentration ◽  
Model Computer ◽  
Two Machines

This paper contains the calculated ground level concentrations of air pollutants from 11 gas turbine models. These calculations were made using Charlotte, N.C. meteorological data. Four of these are simple cycle machines covering a range of size from 5050 hp to 65 MW and four are regenerative machines. Another three are combined cycle (STAG) machines, two machines having unfired and one having a fired heat recovery steam generator. The calculations were made using a slightly modified version of the United States Environmental Protection Agencies Air Quality Display Model Computer Program.

scholarly journals DESIGN AND RESULTS OF AN AI-BASED FORECASTING OF AIR POLLUTANTS FOR SMART CITIES

2021 ◽  
Vol VIII-4/W1-2021 ◽  
pp. 89-96
Author(s):  
L. Petry ◽  
T. Meiers ◽  
D. Reuschenberg ◽  
S. Mirzavand Borujeni ◽  
J. Arndt ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Smart Cities ◽  
Meteorological Data ◽  
Weather Forecast ◽  
Ground Level ◽  
Urban Environments ◽  
Air Pollutant ◽  
Novel Approach

Abstract. This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.

scholarly journals Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

2014 ◽  
Vol 10 (1) ◽  
pp. 29-37
Author(s):  
Wani Tamas ◽  
Gilles Notton ◽  
Christophe Paoli ◽  
Cyril Voyant ◽  
Marie-Laure Nivet ◽  
...  
Keyword(s):  
Air Quality ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Atmospheric Pollutants ◽  
Saharan Dust ◽  
Ann Model ◽  
Deterministic Models ◽  
Artificial Neural ◽  
Dust Events ◽  
Short Term Prediction

Abstract Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France), needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs) that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events). Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

scholarly journals Use of Simulated and Observed Meteorology for Air Quality Modeling and Source Ranking for an Industrial Region

2021 ◽  
Vol 13 (8) ◽  
pp. 4276
Author(s):  
Awkash Kumar ◽  
Anil Kumar Dikshit ◽  
Rashmi S. Patil
Keyword(s):  
Air Quality ◽  
Quality Management ◽  
Environmental Protection Agency ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Ground Level ◽  
Ambient Air ◽  
Industrial Area ◽  
Air Quality Management ◽  
Low Middle Income Countries

The Gaussian-based dispersion model American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is being used to predict concentration for air quality management in several countries. A study was conducted for an industrial area, Chembur of Mumbai city in India, to assess the agreement of observed surface meteorology and weather research and forecasting (WRF) output through AERMOD with ground-level NOx and PM10 concentrations. The model was run with both meteorology and emission inventory. When results were compared, it was observed that the air quality predictions were better with the use of WRF output data for a model run than with the observed meteorological data. This study showed that the onsite meteorological data can be generated by WRF which saves resources and time, and it could be a good option in low-middle income countries (LIMC) where meteorological stations are not available. Also, this study quantifies the source contribution in the ambient air quality for the region. NOx and PM10 emission loads were always observed to be high from the industries but NOx concentration was high from vehicular sources and PM10 concentration was high from industrial sources in ambient concentration. This methodology can help the regulatory authorities to develop control strategies for air quality management in LIMC.

scholarly journals 3D Spatial Analysis of Particulate Matter (PM10, PM2.5 and PM1.0) and Gaseous Pollutants (H2S, SO2 and VOC) in Urban Areas Surrounding a Large Heat and Power Plant

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4070
Author(s):  
Robert Cichowicz ◽  
Maciej Dobrzański
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Power Plant ◽  
Urban Areas ◽  
Ground Level ◽  
Industrial Plant ◽  
Gaseous Pollutants ◽  
Winter Period ◽  
Housing Estate ◽  
The Impact

In many regions of the world, the winter period is a time of poor air quality, due primarily to the increased use of individual and district heating systems. As a consequence, the atmospheric air contains increased concentrations of both particulate matter and gaseous pollutants (as a result of “low” emissions at altitudes of up to 40 m and “high” emissions more than 40 m above ground level). In winter, the increased pollution is very often exacerbated by meteorological conditions, including air temperature, pressure, air speed, wind direction, and thermal inversion. Here, we analyze the concentrations of particulate matter (PM10, PM2.5, and PM1.0) and gaseous pollutants (H2S, SO2, and VOC) in the immediate vicinity of a large solid fuel-fired heat and power plant located in an urban agglomeration. Two locations were selected for analysis. The first was close to an air quality measurement station in the center of a multi-family housing estate. The second was the intersection of two main communication routes. To determine the impact of “low” and “high” emissions on air quality, the selected pollutants were measured at heights of between 2 and 50 m using an unmanned aerial vehicle. The results were compared with permissible standards for the concentration of pollutants. Temperature inversion was found to have a strong influence on the level of pollutants at various heights, with higher concentrations of particulate matter registered at altitudes above 40 m. The source of PM, H2S, and SO2 pollutants was confirmed to be “low emission” from local transport, industrial plant areas, and the housing estate comprising detached houses located in the vicinity of the measuring points. “High emission” was found to be responsible for the high concentrations of VOC at altitudes of more than 40 m above the intersection and in the area of the housing estate.

scholarly journals Impacts of the COVID-19 lockdown on air pollution at regional and urban background sites in northern Italy

2021 ◽  
Vol 21 (10) ◽  
pp. 7597-7609
Author(s):  
Jean-Philippe Putaud ◽  
Luca Pozzoli ◽  
Enrico Pisoni ◽  
Sebastiao Martins Dos Santos ◽  
Friedrich Lagler ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Ground Level ◽  
Northern Italy ◽  
Atmospheric Pollutants ◽  
Aerosol Formation ◽  
Economic Sectors ◽  
Urban Background ◽  
Regional Background

Abstract. The COVID-19 lockdown measures gradually implemented in Lombardy (northern Italy) from 23 February 2020 led to a downturn in several economic sectors with possible impacts on air quality. Several communications claimed in the first weeks of March 2020 that the mitigation in air pollution observed at that time was actually related to these lockdown measures without considering that seasonal variations in emissions and meteorology also influence air quality. To determine the specific impact of lockdown measures on air quality in northern Italy, we compared observations from the European Commission Atmospheric Observatory of Ispra (regional background) and from the regional environmental protection agency (ARPA) air monitoring stations in the Milan conurbation (urban background) with expected values for these observations using two different approaches. On the one hand, intensive aerosol variables determined from specific aerosol characterisation observations performed in Ispra were compared to their 3-year averages. On the other hand, ground-level measured concentrations of atmospheric pollutants (NO2, PM10, O3, NO, SO2) were compared to expected concentrations derived from the Copernicus Atmosphere Monitoring Service Regional (CAMS) ensemble model forecasts, which did not account for lockdown measures. From these comparisons, we show that NO2 concentrations decreased as a consequence of the lockdown by −30 % and −40 % on average at the urban and regional background sites, respectively. Unlike NO2, PM10 concentrations were not significantly affected by lockdown measures. This could be due to any decreases in PM10 (and PM10 precursors) emissions from traffic being compensated for by increases in emissions from domestic heating and/or from changes in the secondary aerosol formation regime resulting from the lockdown measures. The implementation of the lockdown measures also led to an increase in the highest O3 concentrations at both the urban and regional background sites resulting from reduced titration of O3 by NO. The relaxation of the lockdown measures beginning in May resulted in close-to-expected NO2 concentrations in the urban background and to significant increases in PM10 in comparison to expected concentrations at both regional and urban background sites.

scholarly journals Implication of Secondary Atmospheric Pollutants in the Air Quality: A Case-Study for Ozone

2021 ◽  
Author(s):  
David Galán Madruga
Keyword(s):  
Public Health ◽  
Air Quality ◽  
Human Health ◽  
Tropospheric Ozone ◽  
Air Pollutants ◽  
Ground Level ◽  
Atmospheric Pollutants ◽  
Emission Sources ◽  
Rural Environments ◽  
Harmful Effects

Air quality and Public Health are concepts linked to each other. Within the frame of Public Health, a wide range of external factors, derived from rising wastes towards all environmental compartments, may generate harmful effects on human health. In particular, the release of polluting compounds into the ambient air coming from emission sources is a paramount concern, given that atmospheric pollution is considered the most significant environmental risk for human beings. In this context, while this chapter to provide an overview of the most critical air pollutants that can depict air quality status in terms of exposure, potential effects, emission sources, and types of pollutants, the principal purpose is focused on secondary atmospheric pollutants, emphasizing to tropospheric ozone as a significant pollutant within this group. In this sense, aspects such as the atmospheric ozone chemistry responsible for its formation and its spatial distribution into vast territories, including urban, suburban, and rural environments, were conveniently explained. Based on displayed evidence, primaries air pollutants, mainly nitrogen oxides, volatile organic compounds, and carbon monoxide, are responsible for the tropospheric ozone’s formation; therefore, reducing their levels could be translated into a decrease of ozone concentrations at the ground-level. Attending to the ozone distribution, the revealed findings lead to the next concentration gradient: higher ozone levels in rural, followed by suburban and urban sites, respectively. Finally, it can be concluded that the importance of tropospheric ozone within air quality lies in the possibility of producing harmful effects on human health and generating climate changes, either directly or indirectly.

scholarly journals Air quality monitoring and modeling near coal fired power plant

2019 ◽  
Vol 23 (6 Part B) ◽  
pp. 4055-4065
Author(s):  
Bogdana Vujic ◽  
Una Marceta ◽  
Francis Popescu ◽  
Bojana Tot
Keyword(s):  
Air Quality ◽  
Power Plant ◽  
Meteorological Data ◽  
Thermal Power ◽  
Ground Level ◽  
Ambient Air ◽  
Research Area ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Research Period

In municipality of Ugljevik (Bosnia and Herzegovina), the coal-fired thermal power plant is located in the vicinity of the populated area. The ambient air quality monitoring within this area were not systematically performed in the previous period. This research was the first to include indicative measurement of pollutant concentration in air combined with modeling techniques for the purpose of a preliminary assessment of impact which the power plant has on air quality. Since coal, with the sulfur content of 3-6%, is used, as well as the fact that there was no flue gas desulphurization during the research period, this paper shows the results for SO2 as one of the most prominent indicators of pollution originating from the power plant. As a complement to the measurements, modeling of SO2 dispersion was carried out using ADMS5 software. The measurements indicated increased ground-level concentrations of SO2. Additionally, the modeling of SO2 dispersion with real meteorological data was carried out. The modeling confirmed high SO2 concentrations in research area. Also, it was found that the high episodic ground-level SO2 concentrations are the consequence of the terrain configuration and meteorological conditions.

scholarly journals Subtle Changes or Dramatic Perceptions of Air Pollution in Sydney during COVID-19

Environments ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 2
Author(s):  
Peter Brimblecombe ◽  
Yonghang Lai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
New South ◽  
Human Mobility ◽  
Ground Level ◽  
Physical Reality ◽  
Social Perceptions ◽  
Search Terms ◽  
South Wales ◽  
Significant Difference

The COVID-19 pandemic made it critical to limit the spread of the disease by enforcing human isolation, restricting travel and reducing social activities. Dramatic improvements to air quality, especially NO2, have often characterised places under COVID-19 restrictions. Air pollution measurements in Sydney in April 2019 and during the lockdown period in April 2020 show reduced daily averaged NO2 concentrations: 8.52 ± 1.92 and 7.85 ± 2.92 ppb, though not significantly so (p1~0.15) and PM2.5 8.91 ± 4.94 and 7.95 ± 2.64 µg m−3, again a non-significant difference (p1~0.18). Satellite imagery suggests changes that parallel those at ground level, but the column densities averaged over space and time, in false-colour, are more dramatic. Changed human mobility could be traced in increasing times spent at home, assessed from Google Mobility Reports and mirrored in decreased traffic flow on a major road, suggesting compliance with the restrictions. Electricity demand for the State of New South Wales was low under lockdown in early April 2020, but it recovered rapidly. Analysis of the uses of search terms: bushfires, air quality, haze and air pollution using Google Trends showed strong links between bushfires and pollution-related terms. The smoke from bushfires in late 2019 may well have added to the general impression of improved air quality during lockdown, despite only modest changes in the ground level measurements. This gives hints that successful regulation of air quality requires maintaining a delicate balance between our social perceptions and the physical reality.

scholarly journals Spatial Analysis (Measurements at Heights of 10 m and 20 m above Ground Level) of the Concentrations of Particulate Matter (PM10, PM2.5, and PM1.0) and Gaseous Pollutants (H2S) on the University Campus: A Case Study

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 62
Author(s):  
Robert Cichowicz ◽  
Maciej Dobrzański
Keyword(s):  
Spatial Distribution ◽  
Hydrogen Sulfide ◽  
Particulate Matter ◽  
Air Quality ◽  
Spatial Analysis ◽  
Ground Level ◽  
Leeward Side ◽  
High Concentration ◽  
Above Ground Level ◽  
High Concentrations

Spatial analysis of the distribution of particulate matter PM10, PM2.5, PM1.0, and hydrogen sulfide (H2S) gas pollution was performed in the area around a university library building. The reasons for the subject matter were reports related to the perceptible odor characteristic of hydrogen sulfide and a general poor assessment of air quality by employees and students. Due to the area of analysis, it was decided to perform measurements at two heights, 10 m and 20 m above ground level, using measuring equipment attached to a DJI Matrice 600 unmanned aerial vehicle (UAV). The aim of the measurements was air quality assessment and investigate the convergence of the theory of air flow around the building with the spatial distribution of air pollutants. Considerable differences of up to 63% were observed in the concentrations of pollutants measured around the building, especially between opposite sides, depending on the direction of the wind. To explain these differences, the theory of aerodynamics was applied to visualize the probable airflow in the direction of the wind. A strong convergence was observed between the aerodynamic model and the spatial distribution of pollutants. This was evidenced by the high concentrations of dust in the areas of strong turbulence at the edges of the building and on the leeward side. The accumulation of pollutants was also clearly noticeable in these locations. A high concentration of H2S was recorded around the library building on the side of the car park. On the other hand, the air turbulence around the building dispersed the gas pollution, causing the concentration of H2S to drop on the leeward side. It was confirmed that in some analyzed areas the permissible concentration of H2S was exceeded.

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