A simple urban dispersion model tested with tracer data from Oklahoma City and Manhattan

Hanna, Steven R.; Baja, Emmanuel S.

doi:10.1016/j.atmosenv.2008.11.005

Cited by 41 publications

(26 citation statements)

References 11 publications

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“…(PECH was not used in the final analysis because of additional problems). From Watson et al (2006), Allwine and Flaherty (2006b), and Hanna and Baja (2009) Urban dispersion model evaluation 135…”

Section: Ruralmentioning

confidence: 99%

“…PFTs are used in field experiments because they have very low background concentrations and therefore only small amounts of tracer are needed. The table is discussed in more detail by Watson et al (2006), Allwine and Flaherty (2006b), Hanna and Baja (2009). The background C for each PFT is determined from measurements upwind and on the edges of the sampling domain, plus basic knowledge of the backgrounds in the New York City metropolitan area.…”

Section: Introductionmentioning

confidence: 99%

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Acceptance criteria for urban dispersion model evaluation

Hanna¹,

Chang

2012

Meteorol Atmos Phys

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235

124

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The authors suggested acceptance criteria for rural dispersion models' performance measures in this journal in 2004. The current paper suggests modified values of acceptance criteria for urban applications and tests them with tracer data from four urban field experiments. For the arc-maximum concentrations, the fractional bias should have a magnitude\0.67 (i.e., the relative mean bias is less than a factor of 2); the normalized mean-square error should be \6 (i.e., the random scatter is less than about 2.4 times the mean); and the fraction of predictions that are within a factor of two of the observations (FAC2) should be [0.3. For all data paired in space, for which a threshold concentration must always be defined, the normalized absolute difference should be \0.50, when the threshold is three times the instrument's limit of quantification (LOQ). An overall criterion is then applied that the total set of acceptance criteria should be satisfied in at least half of the field experiments. These acceptance criteria are applied to evaluations of the US Department of Defense's Joint Effects Model (JEM) with tracer data from US urban field experiments in Salt Lake City (U2000), Oklahoma City (JU2003), and Manhattan (MSG05 and MID05). JEM includes the SCIPUFF dispersion model with the urban canopy option and the urban dispersion model (UDM) option. In each set of evaluations, three or four likely options are tested for meteorological inputs (e.g., a local building top wind speed, the closest National Weather Service airport observations, or outputs from numerical weather prediction models). It is found that, due to large natural variability in the urban data, there is not a large difference between the performance measures for the two model options and the three or four meteorological input options. The more detailed UDM and the state-of-the-art numerical weather models do provide a slight improvement over the other options. The proposed urban dispersion model acceptance criteria are satisfied at over half of the field experiments.

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Section: Ruralmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acceptance criteria for urban dispersion model evaluation

Hanna¹,

Chang

2012

Meteorol Atmos Phys

Self Cite

235

124

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“…This lack of complete system understanding can lead to predictions which are inaccurate (Dipper et al, 1998). In one example, a Gaussian plume model was compared against tracer data in two urban settings in the USA and found to both over-and under-predict concentrations at different receptors (Hanna and Baja, 2009). There is also evidence that complex prediction leads to a focus on smaller areas of certainty, at the expense of other issues that are no less important, but which cannot be predicted with any certainty; similarly, organisations might make simplifying assumptions that set inappropriately restricted boundaries around the issues to be investigated (Turner, 1976;Stirling, 2010).…”

Section: Impact Assessment Theory and Uncertaintymentioning

confidence: 99%

Managing uncertainty, ambiguity and ignorance in impact assessment by embedding evolutionary resilience, participatory modelling and adaptive management

Bond

Morrison‐Saunders

Gunn

et al. 2015

Journal of Environmental Management

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In the context of continuing uncertainty, ambiguity and ignorance in impact assessment (IA) prediction, the case is made that existing IA processes are based on false 'normal' assumptions that science can solve problems and transfer knowledge into policy. Instead, a 'post-normal science' approach is needed that acknowledges the limits of current levels of scientific understanding. We argue that this can be achieved through embedding evolutionary resilience into IA; using participatory workshops; and emphasizing adaptive management. The goal is an IA process capable of informing policy choices in the face of uncertain influences acting on socio-ecological systems. We propose a specific set of process steps to operationalise this post-normal science approach which draws on work undertaken by the Resilience Alliance. This process differs significantly from current models of IA, as it has a far greater focus on avoidance of, or adaptation to (through incorporating adaptive management subsequent to decisions), unwanted future scenarios rather than a focus on the identification of the implications of a single preferred vision. Implementing such a process * corresponding author 2 would represent a culture change in IA practice as a lack of knowledge is assumed and explicit, and forms the basis of future planning activity, rather than being ignored.

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“…In addition, SF 6 could be detected at low concentrations (50 ppt). That is the reason why other authors have also chosen the SF 6 as a gas tracer for their experimental campaigns (Connan et al et al and Baja [10], Britter et al flow rate from 0.1 to 5.9 g s -1 . An SF 6 cylinder (Messer SA, France) was used and was connected to a mass flowmeter (Sierra 820) calibrated for SF 6 gas.…”

Section: Atmospheric Tracer Releasementioning

confidence: 99%

Experimental study of atmospheric 3D dispersion of a passive tracer in urban environment: comparison with Briggs urban model

et al. 2012

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FluSAP 2010, a part of a large federative research program Vegdud (2010Vegdud ( -2013 funded by the French National Research Agency, is an experimental campaign aiming to improve our knowledge on the dispersion of a plume in heterogeneous urban zones. For this, the French Institute for Radiological Protection and Nuclear Safety (IRSN) conducted an experimental campaign in the city of Nantes (France) to study atmospheric dispersion. A gas tracer SF 6 was released in the atmosphere and measured at different altitudes (from 1 to 100 m) by using a mast and a small tethered balloon. This allows determining the plume vertical dispersion in an urban area as a function of atmospheric turbulence. A sampling system was also used at ground (1 m height) in order to evaluate the plume horizontal dispersion. All these systems were placed at a distance from the emission point between 20 and 1150 m: 30 SF 6 emissions were performed between May 18 and May 27, 2010.High compatibility was found between experimental horizontal and vertical widths of the plume and Briggs urban model for the stability class B, C and D according to Pasquill classification. However, this compatibility was only confirmed for small distances from the source (till 370 m). For higher distances from the source, it is hard to draw significant conclusions.

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A simple urban dispersion model tested with tracer data from Oklahoma City and Manhattan

Cited by 41 publications

References 11 publications

Acceptance criteria for urban dispersion model evaluation

Acceptance criteria for urban dispersion model evaluation

Managing uncertainty, ambiguity and ignorance in impact assessment by embedding evolutionary resilience, participatory modelling and adaptive management

Experimental study of atmospheric 3D dispersion of a passive tracer in urban environment: comparison with Briggs urban model

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