urban dispersion for the 21st century - wit press...urban dispersion for the 21st century b. b....

Urban dispersion for the 21st century

B. B. Hicks Air Resources Laboratory, NOAA, U.S.A.

Abstract

The threat of a terrorist attack using gaseous or biological agents has changed the focus of urban dispersion research programs. No longer are the studies being conducted solely in intensive programs to explore specific aspects identified by slowly evolving numerical simulations. Instead, there is an emerging parallel thrust to optimize the use of existing data and to provide forecasts based heavily on data assimilation. In this context, there is a basic rule that appears to be emerging: to maximize the accuracy of predictions, minimize the reach beyond reliable observations. Within an urban canopy (i.e. in the street canyons) the complexity of transport through the air is such that an accurate prediction of concentrations at any specific place and time is unlikely, regardless of the proximity of accurate meteorological data. Some options are reviewed, as are currently being tested in Washington D.C. and in New York City. Keywords: urban dispersion, emergency response,

1 Introduction

There are many computer models that purport to describe dispersion in urban areas. Many of these yield displays that suggest confidence in the outputs that is not easily reconciled with the realities involved. With few exceptions, data to verify the accuracy of forecasts are not available. In those cases where data are available, the agreement between model predictions and reality can sometimes be poor (see Gryning and Lyck, [2]; Draxler, [1]). Often, confidence is generated on the basis of comparisons against data obtained in experiments usually conducted elsewhere, and often in circumstances selected to satisfy requirements of the models. In other words, the models are often tested in situations such that there is a good chance that there will be success. The chances that the circumstances of field tests mirror the circumstances of an actual event are slim. Hence, there is a credibility gap that needs to be addressed.

© 2005 WIT Press WIT Transactions on The Built Environment, Vol 82, www.witpress.com, ISSN 1743-3509 (on-line)

Safety and Security Engineering 555

The matter is of immediate concern because of the recognized vulnerability of cities and urban areas to attacks using hazardous gases and biological agents. As time progresses, research programs to develop improved models are slowly changing their focus. In the past, the emphasis of such programs has been on revealing details of the processes involved and on steadily improving the ability of numerical models to simulate the observed behaviour. Today, the emphasis is on operationalizing methods that give accurate guidance, regardless of the specific location, or time. To this end, use must be made of local data. There are two different areas of concern, in both of which there is need to extend beyond current practice in order to derive the capabilities that are now needed. First, there is a need to refine understanding of so-called skimming flow – the flow from far upwind across the urban area or city of concern. Second, there is need to address the matter of canopy dispersion – the spreading of materials through the air around the buildings and in the street canyons. In both cases, there is need to consider two aspects – the flow fields and dispersion that can be predicted on an event basis by fully deterministic models, and the stochastic fluctuations in both time and space that are beyond the reach of such deterministic models (q.v. Lorenz, [3]).

2 Skimming flow

It is well recognized that modern mesoscale meteorological models can simulate the wind flow across an urban area quite well, on the average. However, it is not the average situation that we are trying to address but some specific case that cannot be anticipated in detail. There is a question that then arises – How can we make best use of local meteorological data, and how might existing data sets be augmented to provide the basis for a better dispersion forecast? In practice, typical urban areas and major cities have an existing meteorological monitoring infrastructure already in existence, operated by such organizations and local governments, transportation authorities, environmental protection agencies, etc., with a likelihood that several such local networks might well be operated by private and/or commercial entities. Such local networks are usually designed to provide information dedicated to the needs of the host authority – the detection of freezing conditions for transportation departments, for example. In general the networks employ standard meteorological sensing systems, usually extending to wind speed and direction, and sometimes with turbulence variables as well. The role of data collected by such networks is not immediately clear. In practice, the flow aloft is largely determined by meso-scale factors associated with the prevailing meteorological conditions. A site selection criterion is therefore imposed, so that the data collected might be considered representative of the area affecting the mesoscale flow patterns. In most North American cities, the usual “local” meteorological observations reported by the National Weather Service refer to a local airport, often many tens of kilometres away from the urban area in question. An intriguing exception is Washington, DC, where local meteorological data are gathered at an airport


556 Safety and Security Engineering

(Washington Reagan National Airport) only a few kilometres from the downtown area. This particular airport is situated on the bank of a river – the Potomac. On first principles, it might be expected to find that prevailing wind directions are parallel to the river, and indeed this is found to be the case. But the question then arises as to whether these same wind directions are appropriate for the downtown area some few kilometres distant. Figure 1 shows the answer. Wind roses are shown, collected over a three-month summer period in 2004 using data from the airport and also from a specialized array of meteorological observing systems deployed across the downtown area (referred to as “DCNet,” see http://dcnet.atdd.noaa.gov). Clearly, on the average there are substantial differences between the wind field experienced downtown and those reported from the local airport.

Figure 1: Wind roses derived from meteorological observations made at Washington Reagan National Airport and at three sites in the nearby national capital area. Data are for three months – August, September and October. The westernmost data were obtained near the Lincoln Memorial; the northernmost near the White House.

The difference in wind roses for Washington and for the nearby airport is striking. Clearly, classical assessments of the effects of dispersion have been wrong, since all have been based on National Airport data. A release of some hazardous material into the air would be likely to have the most widespread effect if it occurs near dawn. Classical meteorological thinking indicates that the atmosphere would then be stably stratified and the hazardous



material would not be rapidly diluted by vertical mixing. In fact, the Washington DC specialized data show that this expectation is possibly erroneous. DCNet observing systems carry sensors to measure the rate of heat exchange between the surface and the atmosphere. It is this heat flux that controls atmospheric stability. When the heat loss from the surface is strong, as in cloudless days in summer, then convection is vigorous and dilution is rapid. At night, classical thinking would predict that the cooling of the surface would result in net heat transfer from the air to the surface underneath, leading to stably stratified flow and minimal dilution in the vertical.

Figure 2: A typical daily cycle of the rate of loss of heat from the surface, measured as the covariance between vertical wind velocity (w) and temperature (T): H = w'T' (m.ºC/s). Data are for a parkland in Washington (a), downtown Washington (b), and midtown Manhattan, New York City (c).

DCNet data show the error of this classical thinking. Figure 2 shows three plots of the rate of heat loss from the surface by atmospheric mixing – (a) for a parkland near downtown Washington, DC, (b) for a city area in Washington DC, and (c) for a city area in New York (southern Manhattan). The results indicate that the heat fluxes measured over the parkland (in fact – the Washington arboretum, see plot (a)) demonstrate the expectations of conventional meteorology. At night, the heat flux was towards the surface, not away from it. But this is not the case for the downtown area at the same time (plot (b)). It appears, therefore, that heat either stored in the urban canopy or generated by air conditioning systems or traffic causes the atmosphere over the downtown area to



remain unstable, even at night. The third illustration (c) shows that this is the case for areas of Manhattan, as well. For the New York case, the night time positive heat fluxes are much greater than for the Washington case. This is not surprising, considering the much larger dimensions of the buildings and their much closer proximity to each other in New York. There are good reasons, therefore, to employ local data as a boundary condition for the forecasting models that emergency managers currently require. The examples given above are derived from a specialized network, DCNet, set up to demonstrate the need. There are many existing meteorological observations made in the same area, that have not been used in this analysis. The reason for their omission at this stage is simple – their siting remains uncertain, and the quality of the data from them is as yet unproven. It remains to examine these existing data sources more closely. However, one particular aspect is clear. The differences in heat fluxes are such that some direct measurements of them would probably be very useful. Such measurements require the use of a three-dimensional, fast response anemometer. These devices are recently developed, and their use in existing networks is rare. Inclusion of local observations in conventional dispersion forecasting systems can yield surprising results. Predictions of plume dispersion made without using local data can be completely misleading, with differences in direction typically of the order of 90 degrees. Moreover, the assumption usually made that flow may be stably stratified at night (with minimal vertical diffusion) might well be wrong. A way is needed, therefore, to assimilate such local data as might provide the information necessary to remedy these errors. Data assimilation methods are needed, and techniques to extract such key information as area-specific diffusion rates need to be developed.

3 Canopy dispersion

It is often not the flow aloft that is the key issue, but rather the situation within the street canyons, where people will be exposed. Details of the flow aloft might well be useful in determining how released material will move through the street canyons, but the details will be strongly influenced by the local street characteristics – the width, depth, and uniformity of the street canyons, and the rate at which turbulence is generated by such factors as the release of heat from the buildings and traffic density. In the end, all of these considerations will certainly be addressable with deterministic methodologies. The requirement of today is for useful products now. The science needs to propose interim solutions. Much of what is now known has been based on model studies, either using physical models in wind tunnels of computational fluid dynamics models in computers. There has been a long history of tracer studies in urban areas. With rare exceptions, the focus has been on developing databases to test model predictions in conditions compatible with model assumptions. Such studies are preferred by the scientific community, since they feed the process by which models evolve – a continuing series of model improvements, tests, and further



model improvements. In the end, the product will be precisely the capability that is desired -- a widely applicable methodology to forecast the dispersion from any source at any place and time. In the meantime, society is faced with the immediate need for dispersion forecasts that are site-specific and time dependent. Moreover, the dominant need is for methods that will address the exposure of people – within the street canyons rather than in the skimming flow above them. The complexity of the situation is clear. Flow patters are influenced by the skimming flow aloft, by the distribution, configuration and orientation of the buildings, by traffic, and by heat generated by the heating and cooling systems of the buildings. Heat storage by the buildings during the daytime and release at night will also be a factor, as has been demonstrated above (see Figure 2). It is again relevant to consider the role of in situ sensors, i.e. meteorological observing systems located within the urban street canyon complex rather than above it. These cannot be used to help define the skimming flow aloft, because they are not in locations that can be considered regionally representative. However, they do indeed report observations indicative of local situations, and it is these local conditions to which people are exposed. Making use of their data will be a challenge, one that is presently being addressed in wind tunnel studies and in computational fluid dynamics numerical modelling. The final results of such studies will be a long time arriving, but society has immediate needs. There has been a long history of tracer studies conducted in urban areas and in cities. These studies usually utilize sulphur hexafluoride (SF6) or some similar trace gas that is very easily detected in minute quantities. The historic studies conducted at locations such as Salt Lake City, London, Basel, Marseilles, Oklahoma City, and Los Angeles have often been short-term intensive examinations of how SF6 or some other tracer material disperses in a given set of conditions. Results are then used to refine models, and the improved models are then tested in another series of intensive studies. An exception was the METREX study of dispersion in Washington, DC, in that it extended over a long period and involved regular releases of several different tracers in an attempt to cover the complete range of conditions likely to be encountered (see Draxler, 1987). Even though the study was 15 months long, it was basically an extended series of intensive studies. All such studies have needed to confront the issue of the SF6 background. In practice, SF6 is used as an insulator in high voltage transformers. Many urban substations rely on the use of SF6, and some SF6 leaks. There are substations leaking SF6 in most major US cities. Two are of major relevance here – one is in central Washington, DC. The other is upwind of central Manhattan, New York City. Figure 3 shows the results of a survey of SF6 levels at street level, downwind of the eastern Manhattan substation. Air was sampled continuously during the survey, using a mobile sampling system in the rear of a van. The results were obtained using a mobile analysis system developed by researchers of the Air Resources Laboratory of NOAA,. Located in Idaho Falls, Idaho. The system has a detection limit of five parts per trillion by volume. The survey illustrated was conducted at night, between 2030 and 2315 hours on 22 January, 2004. During this short a period, the emission rate could probably be assumed



constant. However, the local concentration maxima inside the street canyons varied considerably, dropping by an order of magnitude from immediately downwind of the source to 2 km distant.

Figure 3: Results of a street-level survey of SF6 levels downwind of a source "of opportunity" in midtown New York City. Red indicates streets where SF6 was detected. Green circles show concentration maxima.

The surveys conducted so far, in both New York and in Washington, DC, demonstrate that the spread of released material through the urban street canopies varies remarkably with time of day and with the meteorological circumstance. Indeed, such has been the conclusion of all urban dispersion studies. It is clear that dispersion within the urban canopy is complex, and will be poorly described by classical deterministic meteorological models. Current efforts are addressing a simple approach to providing the answer that emergency responders want, sidestepping the matter of these intimidating uncertainties. It appears feasible to forecast the probability that specific exposure levels might be exceeded, rather than forecasting the concentration fields themselves. At present, such thinking is in its infancy, but its promise is exceedingly attractive. Once a probabilistic framework is accepted, consideration of exposures inside buildings could constitute a straightforward extension of the approach rather than a separate issue of intimidating complexity.



4 Discussion and conclusions

There are different levels of information that would be required by a first responder. In the first stages of a response, details of the source will not be available. The most important information required would then be details of safe routes by which response personnel can gain access to the area of the incident, and of safe corridors for the evacuation of those people who must be relocated. Identifications of safe corridors does not require detailed information on the nature of the source. As soon as possible, information regarding the source term should be obtained and used as input in more advanced dispersion models. It is the guidance from these “second-stage” models that will delineate the extent of the area of concern, and be used to design optimal response strategies. In both stages, there will be need to consider both the behaviour within the street canyons and the dispersion meteorology of flow across the urban area – so-called skimming flow. A central consideration is how meteorological forecasts and mesonetwork observations should be combined in order to produce an optimal result. In this regard, it is appreciated that modern mesoscale models have the inherent capability to simulate the changes in flow aloft due to the presence of buildings and other structural elements at the surface. The accuracy of the predictions is illustrated by comparisons of averages constructed in different circumstances. If the interest is in the ability of such mesoscale models to reproduce, with accuracy, the behaviour for a single specific event, then the level of confidence in the predictive models diminishes. Data assimilation becomes a dominant concern. There is a guiding rule. For the most accurate forecasts, minimize the extrapolation of the forecasts beyond available observations. There is, then, a clear distinction between the goals of model developers working on (a) dispersion forecasting methods for US cities and (b) dispersion predictions for areas with no available on-scene data (such as would be the likely scenarios encountered by an expeditionary force). The difference can be expressed simply. The US is data rich, and the challenge to modellers is to learn how to make optimal use of the available data. At the opposite extreme, the situations likely to be encountered by armed forces operating on foreign soil will likely be data poor. The model requirements are correspondingly different and the field experimentation should reflect this difference. Most urban areas have existing networks of meteorological sensors, providing a potential resource of enormous promise. However, the quality of the data derived from these existing networks is clearly a major concern. Augmenting these existing arrays to satisfy the needs of dispersion forecasting can be quite inexpensive, but very productive. This is particularly so because most of the existing networks are set up for purposes quite different from the dispersion applications of present interest. It is clear that their data should be used only with care, and with full recognition of their limitations. The existence of sources “of opportunity” provides an opportunity to evaluate the models for any particular area, and to assess the role of local observations objectively. Planning



studies would certainly benefit. Perhaps more importantly, the addition of a few specialized meteorological systems reporting data critical for the computation of dispersion rather than of the standard meteorological variables (temperature, etc.) could well be critical. City planners of the future will probably be faced with a requirement for meteorological observations necessary to ensure that the environment is truly safe for its population to live and work. Existing data sources must then be reviewed, and augmented to satisfy the demands not only of the dispersion models then accepted but also of the improved models then being developed. As in the case of DCNet, carefully located specialized sensors can contribute to the production of greatly improved dispersion forecasts, with refinement in both the direction of a skimming flow plume and the rate of dilution of it by turbulence. For street canyon concerns, the presence of tracer sources of opportunity offers the possibility of continuous comparison between model predictions and observations made at some specifically selected locations. While not providing direct indications of the behaviour expected for a release from some other location in the same city, this comparison would build confidence in the applicability of the model before it is relocated to address the specific issue of concern. There are, therefore, strong reasons to consider ways in which models can be enhanced by drawing on data sources and other features of urban areas and cities that are not yet widely considered.

Acknowledgement

This work is a contribution to the NOAA program on Urban Atmospheric Research, under the auspices of the NOAA Office of Homeland Security.

References

[1] Draxler, R. R., 1987. One year of tracer dispersion measurements over Washington, D.C. Atmos. Environ, 21, 69 – 77.

[2] Gryning, S. E., and E, Lyck, 1984. Atmospheric dispersion from elevated sources in an urban area: comparison between tracer experiments and model calculations. J. Climate and Appl. Meteorol, 23, 651 – 660.

[3] Lorenz, E. N., 1969. The predictability of a flow which possesses many scales of motion. Tellus 231, 289 – 307.



urban dispersion for the 21st century - wit press...urban dispersion for the 21st century b. b....

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