Paper III: Plume height measurements at Northfleet and Tilbury power stations

Hamilton, P.M.

doi:10.1016/0004-6981(67)90054-6

Cited by 25 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For cases when the wind direction was not perpendicular to the camera orientation, they were not able to measure the plume rise correctly. In other studies, Hamilton (1967) and Bacci et al (1974) also measured plume rise using lidar and related their data to the meteorological variables and power-station operating conditions to show that the plume-rise measurements were in agreement with the models developed by Lucas et al (1963) and Briggs (1969).…”

Section: Introductionmentioning

confidence: 50%

Rise of Buoyant Emissions from Low-Level Sources in the Presence of Upstream and Downstream Obstacles

Pournazeri

Princevac

Venkatram

2012

Boundary-Layer Meteorol

View full text Add to dashboard Cite

Field and laboratory studies have been conducted to investigate the effect of surrounding buildings on the plume rise from low-level buoyant sources, such as distributed power generators. The field experiments were conducted in Palm Springs, California, USA in November 2010 and plume rise from a 9.3 m stack was measured. In addition to the field study, a laboratory study was conducted in a water channel to investigate the effects of surrounding buildings on plume rise under relatively high wind-speed conditions. Different building geometries and source conditions were tested. The experiments revealed that plume rise from low-level buoyant sources is highly affected by the complex flows induced by buildings stationed upstream and downstream of the source. The laboratory results were compared with predictions from a newly developed numerical plume-rise model. Using the flow measurements associated with each building configuration, the numerical model accurately predicted plume rise from low-level buoyant sources that are influenced by buildings. This numerical plume rise model can be used as a part of a computational fluid dynamics model.

show abstract

Section: Introductionmentioning

confidence: 50%

Rise of Buoyant Emissions from Low-Level Sources in the Presence of Upstream and Downstream Obstacles

Pournazeri

Princevac

Venkatram

2012

Boundary-Layer Meteorol

View full text Add to dashboard Cite

show abstract

“…In the early days, many researchers 24–32 employed full-scale on-site measurements for examining the pollutant dispersion from power stations, 24–28,32 urban traffic, 29,33–36 industrial complexes 32 and laboratories. 35 Lucas et al.…”

Section: On-site Measurementsmentioning

confidence: 99%

“…Increased chimney height did not necessarily produce increased plume rise. 27 Similarly, Barynin and Wilson 32 employed fast-response flame-photometric detector for recording the concentration of sulphur compounds in air 5 km downwind of West Burton Power Station on a rooftop of Imperial College, London. The rapidly fluctuating concentration had dramatically influenced the human response, though it could be quickly reduced by adaptation.…”

Section: On-site Measurementsmentioning

confidence: 99%

Dispersion of air pollutants around buildings: A review of past studies and their methodologies

Xia

Niu

2012

Indoor and Built Environment

View full text Add to dashboard Cite

The review shows that on-site measurement has the benefit of obtaining real life data under real atmospheric boundary layer conditions, but it is too difficult to capture the real atmospheric conditions and too expensive to conduct such experiment as too many samplers and receptors are needed, particularly for investigating the air pollutant issues with regard to building arrays. This difficulty can be solved by physical-scale modelling, which can provide more controllable airflow boundary conditions, but accuracy is compromised by its inability to simulate real boundary layer turbulence conditions and buoyancy effects. Water channel is advantageous in buoyancy experiments but maintaining realistic viscosity and density properties of the medium could be challenging. Direct numerical simulation can provide accurate results; but it is impractical to simulate flows in large domains due to high computational requirements. Reynold-averaged Navier–Stokes numerical simulation is the most commonly used model for pollutant dispersion and infection control analysis but generally over-predicts surface concentrations at the leeward side of a building. Although large eddy simulation can over-predict the lateral pollutant concentration in the wake region of the building, flow unsteadiness and intermittency can be solved.

show abstract

“…However, subsequent early evaluations of the accuracy of these parameterizations (cf. VDI, 1985) have had mixed results, including parameterization estimates averaging 50% higher than observations (Giebel, 1979); within 12 and 50% of observations (Ritmann, 1982); 30% higher than observations (England et al, 1976); 50% higher than 10 observations (Hamilton, 1967). Recent studies using Reynolds averaged Navier-Stokes and large eddy simulation (RAND-LES) modelling have shown that the integral model of Briggs overestimates the plume rise and its overestimation error increases as the role of atmospheric turbulence increases (Ashrafi et al, 2017), and underestimates of plume rise, inferred from excessively high predicted surface concentrations (Webster and Thompson, 2002).…”

Section: Some Of the Potential Reasons For This Poor Performance Inclmentioning

confidence: 99%

A chemical transport model study of plume rise and particle size distribution for the Athabasca oil sands

Akingunola¹,

Makar²,

Zhang³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract.We evaluate four high-resolution model simulations of pollutant emissions, chemical transformation and downwind transport for the Athabasca oil sands using the Global Environmental Multiscale -Modelling Air-quality and Chemistry (GEM-MACH) model using surface monitoring network and aircraft observations of multiple pollutants, for simulations spanning a 15 time period corresponding to an aircraft measurement campaign in the region in summer 2013. We have focussed here on the impact of different representations of the model's aerosol size distribution and plume-rise parameterization on model results. The use of a more finely resolved representation of the aerosol size distribution was found to have a significant impact on model performance, reducing the magnitude of the original surface PM 2.5 negative biases by 32%. 20We compared model predictions of SO 2 , NO 2 , and speciated particulate matter concentrations from simulations employing the commonly-used Briggs (1984) plume-rise algorithms to redistribute emissions from large stacks with stack plume observations. As in our companion paper (Gordon et al., 2018), we found these algorithms resulted in under-predictions of plume rise, with 39 to 60% of predicted plume heights falling below half of the observed plume heights. However, we found here that a layered buoyancy approach for stable to neutral atmospheres, coupled with the assumption of free rise in 25 convectively unstable atmospheres, resulted in much better model performance, both for atmospheric constituent concentrations and the predicted height of the plumes. Persistent issues with over-fumigation of plumes in the model were linked to positive biases in the predicted temperatures between the surface and 1km elevation. These in turn may lead to overestimates of near-surface diffusivity, resulting in excessive fumigation.Atmos. Chem. Phys. Discuss., https://doi

show abstract

Paper III: Plume height measurements at Northfleet and Tilbury power stations

Cited by 25 publications

References 0 publications

Rise of Buoyant Emissions from Low-Level Sources in the Presence of Upstream and Downstream Obstacles

Rise of Buoyant Emissions from Low-Level Sources in the Presence of Upstream and Downstream Obstacles

Dispersion of air pollutants around buildings: A review of past studies and their methodologies

A chemical transport model study of plume rise and particle size distribution for the Athabasca oil sands

Contact Info

Product

Resources

About