Gaussian plume modeling of contaminant transport

Abstract. Summit, Greenland is a remote Arctic research station allowing for field measurements at the highest point of the Greenland Ice Sheet. Due to the current reliance on diesel generators for electricity at Summit, unavoidable local emissions are a potential contamination threat to the measurement of combustion-related species in the air and snow. The effect of fossil-fuel combustion on particulate elemental carbon (EC) is assessed by a combination of ambient measurements (~1 km from the main camp), a series of snow pits, and Gaussian plume modeling. Ambient measurements indicate that the air directly downwind of the research station generators experiences particulate absorption coefficient (closely related to EC) values that are up to a factor of 200 higher than the summer 2006 non-camp-impacted ambient average. Local anthropogenic influence on snow EC content is also evident. The average EC concentration in 1-m snow pits in the "clean air" sector of Summit Camp are a factor of 1.8–2.4 higher than in snow pits located 10 km and 20 km to the north ("downwind") and south ("upwind") of the research site. Gaussian plume modeling performed using meteorological data from years 2003–2006 suggests a strong angular dependence of anthropogenic impact, with highest risk to the northwest of Summit Camp and lowest to the southeast. Along a transect to the southeast (5 degree angle bin), the modeled frequency of significant camp contribution to atmospheric EC (i.e. camp-produced EC>summer 2006 average EC) at a distance of 0.5 km, 10 km, and 20 km is 1%, 0.2%, and 0.05%, respectively. According to both the snow pit and model results, a distance exceeding 10 km towards the southeast is expected to minimize risk of contamination. These results also suggest that other remote Arctic monitoring stations powered by local fuel combustion may need to account for local air and snow contamination in field sampling design and data interpretation.

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Evaluating local anthropogenic impact on remote Arctic monitoring stations: a case study at Summit, Greenland

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-1239-2008 ◽

2008 ◽

Vol 8 (1) ◽

pp. 1239-1259

Author(s):

G. S. W. Hagler ◽

M. H. Bergin ◽

E. A. Smith ◽

M. Town ◽

J. E. Dibb

Keyword(s):

Anthropogenic Impact ◽

Meteorological Data ◽

Greenland Ice Sheet ◽

Field Measurements ◽

Fuel Combustion ◽

Research Station ◽

Plume Modeling ◽

Gaussian Plume ◽

Ambient Measurements ◽

Monitoring Stations

Abstract. Summit, Greenland is a remote Arctic research station allowing for field measurements at the highest point of the Greenland Ice Sheet. Due to the current reliance on diesel generators for electricity at Summit, unavoidable local emissions are a potential contamination threat to the measurement of combustion-related species in the air and snow. The effect of fossil-fuel combustion on particulate elemental carbon (EC) is assessed by a combination of ambient measurements (~1 km from the main camp), a series of snow pits (up to 20 km from Summit Camp), and Gaussian plume modeling. Ambient measurements indicate that the air directly downwind of the research station generators experiences particulate absorption coefficient (closely related to EC) values that are up to a factor of 200 higher than the summer 2006 non-camp-impacted ambient average. Local anthropogenic influence on snow EC content is also evident. The average EC concentration in 1-m snow pits in the "clean air" sector of Summit Camp are a factor of 1.8–2.4 higher than in snow pits located 10 km and 20 km to the north ("downwind") and south ("upwind") of the research site. Gaussian plume modeling performed using meteorological data from years 2003–2006 suggests a strong angular dependence of anthropogenic impact, with highest risk to the northwest of Summit Camp and lowest to the southeast. Along a transect to the southeast (5 degree angle bin), the modeled frequency of significant camp contribution to atmospheric EC (i.e. camp-produced EC>2006 summer average EC) at a distance of 0.5 km, 10 km, and 20 km is 1%, 0.2%, and 0.05%, respectively. According to both the snow pit and model results, a distance exceeding 10 km towards the southeast is expected to minimize risk of contamination. These results also suggest that other remote Arctic monitoring stations powered by local fuel combustion may need to account for local air and snow contamination in field sampling design and data interpretation.

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Toxic Gas Continuity Diffusion Algorithm of Multi-Tanks Based on Meshing Technique

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.581-582.261 ◽

2012 ◽

Vol 581-582 ◽

pp. 261-264

Author(s):

Hong De Wang ◽

Tie Jun Cui

Keyword(s):

Influence Factors ◽

Technological Support ◽

Tank Farm ◽

Noxious Substance ◽

Chemical Industrial Park ◽

Distribution Process ◽

Plume Modeling ◽

Gaussian Plume ◽

Toxic Gas ◽

And Control

According to the techniques of the mashing and the Gaussian plume modeling, combining all kinds of the factors variation, such as the tanks leak velocity, the leak height, the wind direction and speed, and the environmental stabilization grade, the concentration distributing rules of the toxic gas continuous diffusion in the tank farm are simulated. Firstly, focusing on the central of the tank farm in chemical industrial park, mashes the tank farm as the research units for quantitative analysis and superposition calculation. Secondly, constructs the diffusion concentration distributing model of the toxic gas leak based on the Gaussian plume theory. Finally, by the condition of the single-leak hazard installation and the multiple-leak major hazard installations, the toxic gas continuous diffusion algorithms are studied, viz. in accordance with any of the influence factors variation, carve up the time sections, and then basing on Matlab technique, integrating the concentration superposition effects, simulate the concentrated distribution process of the noxious substance during the influence fields. Taking ammonia gas tank farm leakage as example, the simulation results show that the method may provide technological support for major hazard prevention and control in chemical industry park.

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