Should we be ‘Leaf’-ing out vegetation when parameterising the aerodynamic properties of urban areas?

Email: C.W.Kent@pgr.reading.ac.uk

When modelling urban areas, vegetation is often ignored in attempt to simplify an already complex problem. However, vegetation is present in all urban environments and it is not going anywhere… For reasons ranging from sustainability to improvements in human well-being, green spaces are increasingly becoming part of urban planning agendas. Incorporating vegetation is therefore a key part of modelling urban climates. Vegetation provides numerous (dis)services in the urban environment, each of which requires individual attention (Salmond et al. 2016). However, one of my research interests is how vegetation influences the aerodynamic properties of urban areas.

Two aerodynamic parameters can be used to represent the aerodynamic properties of a surface: the zero-plane displacement (zd) and aerodynamic roughness length (z0). The zero-plane displacement is the vertical displacement of the wind-speed profile due to the presence of surface roughness elements. The aerodynamic roughness length is a length scale which describes the magnitude of surface roughness. Together they help define the shape and form of the wind-speed profile which is expected above a surface (Fig. 1).

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Figure 1: Representation of the wind-speed profile above a group of roughness elements. The black dots represent an idealised logarithmic wind-speed profile which is determined using the zero-plane displacement (zd) and aerodynamic roughness length (z0) (lines) of the surface.

For an urban site, zd and z0 may be determined using three categories of methods: reference-based, morphometric and anemometric. Reference-based methods require a comparison of the site to previously published pictures or look up tables (e.g. Grimmond and Oke 1999); morphometric methods describe zd and z0 as a function of roughness-element geometry; and, anemometric methods use in-situ observations. The aerodynamic parameters of a site may vary considerably depending upon which of these methods are used, but efforts are being made to understand which parameters are most appropriate to use for accurate wind-speed estimations (Kent et al. 2017a).

Within the morphometric category (i.e. using roughness-element geometry) sophisticated methods have been developed for buildings or vegetation only. However, until recently no method existed to describe the effects of both buildings and vegetation in combination. A recent development overcomes this, whereby the heights of all roughness elements are considered alongside a porosity correction for vegetation (Kent et al. 2017b). Specifically, the porosity correction is applied to the space occupied and drag exerted by vegetation.

The development is assessed across several areas typical of a European city, ranging from a densely-built city centre to an urban park. The results demonstrate that where buildings are the dominant roughness elements (i.e. taller and occupying more space), vegetation does not obviously influence the calculated geometry of the surface, nor the aerodynamic parameters and the estimated wind speed. However, as vegetation begins to occupy a greater amount of space and becomes as tall as (or larger) than buildings, the influence of vegetation is obvious. Expectedly, the implications are greatest in an urban park, where overlooking vegetation means that wind speeds may be slowed by up to a factor of three.

Up to now, experiments such as those in the wind tunnel focus upon buildings or trees in isolation. Certainly, future experiments which consider both buildings and vegetation will be valuable to continue to understand the interaction within and between these roughness elements, in addition to assessing the parameterisation.

References

Grimmond CSB, Oke TR (1999) Aerodynamic properties of urban areas derived from analysis of surface form. J Appl Meteorol and Clim 38:1262-1292.

Kent CW, Grimmond CSB, Barlow J, Gatey D, Kotthaus S, Lindberg F, Halios CH (2017a) Evaluation of Urban Local-Scale Aerodynamic Parameters: Implications for the Vertical Profile of Wind Speed and for Source Areas. Boundary-Layer Meteorology 164: 183-213.

Kent CW, Grimmond CSB, Gatey D (2017b) Aerodynamic roughness parameters in cities: Inclusion of vegetation. Journal of Wind Engineering and Industrial Aerodynamics 169: 168-176.

Salmond JA, Tadaki M, Vardoulakis S, Arbuthnott K, Coutts A, Demuzere M, Dirks KN, Heaviside C, Lim S, Macintyre H (2016) Health and climate related ecosystem services provided by street trees in the urban environment. Environ Health 15:95.

Experiences of the NERC Atmospheric Pollution and Human Health Project.

Email: k.m.milczewska@pgr.reading.ac.uk

One of the most exciting opportunities of my PhD experience to date has been a research trip to Beijing in June, as part of the NERC Atmospheric Pollution and Human Health (APHH) project. This is a worldwide research collaboration with a focus on the way air pollution in developing megacities affects human health, and the meeting in Beijing served as the 3rd project update.

Industrialisation of these cities in the last couple of decades has caused air pollution to rise rapidly and regularly exceed levels deemed safe by the World Health Organisation (WHO).  China sees over 1,000,000 deaths annually due to particulate matter (PM), with 76 deaths per 100,000 capita. In comparison, the UK has just over 16,000 total deaths and 26 per capita. But not only do these two countries have very different climates and emissions; they are also at very different stages of industrial development. So in order to better understand the many various sources of pollution in developing megacities – be they from local transport, coal burning or advected from further afield – there is an increased need for developing robust air quality (AQ) monitoring measures.

The APHH programme exists as a means to try and overcome these challenges. My part in the meeting was to expand the cohort of NCAS / NERC students researching AQ in both the UK and China, attending a series of presentations in a conference-style environment and visiting two sites with AQ monitoring instruments. One is situated in the Beijing city centre while the other in the rural village of Pinggu, just NW of Beijing. Over 100 local villagers take part in a health study by carrying a personal monitor with them over a period of two weeks. Their general health is monitored at the Pinggu site, alongside analysis of the data collected about their personal exposure to pollutants each day, i.e. heatmaps of different pollutant species are created according to GPS tracking. Having all the instruments being explained to us by local researchers was incredibly useful, because since I work with models, I haven’t had a great deal of first hand exposure to pollutant data collection. It was beneficial to get an appreciation of the kind of work this involves!

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In between all our academic activities we also had the chance to take some cultural breaks – Beijing has a lot to offer! For example, our afternoon visit to the Pinggu rural site followed the morning climb up the Chinese Great Wall. Although the landscape was somewhat obscured by the pollution haze, this proved to be a positive thing as we didn’t have to suffer in the direct beam of the sun!


I would like to greatly thank NERC, NCAS and University of Leeds for the funding and organisation of this trip. It has been an incredible experience, and I am looking forward to observing the progess of these projects, hopefully using what I have learnt in some of my own work.

For more information, please visit the APHH student blog in which all the participants documented their experiences: https://www.ncas.ac.uk/en/introduction-to-atmospheric-science-home/18-news/2742-ncas-phd-students-visit-four-year-air-quality-fieldwork-project-in-beijing

Understanding the urban environment and its effect on indoor air.

Email: h.l.gough@pgr.reading.ac.uk

Recent estimates by the United Nations (2009) state that 50 to 70 % of the world’s population now live in urban areas with over 70 % of our time being spent indoors, whether that’s at work, at home or commuting.

We’ve all experienced a poor indoor environment, whether it’s the stuffy office that makes you sleepy, or the air conditioning unit that causes the one person under it to freeze. Poor environments make you unproductive and research is beginning to suggest that they can make you ill. The thing is, the microclimate around one person is complex enough, but then you have to consider the air flow of the room, the ventilation of the building and the effect of the urban environment on the building.

So what tends to happen is that buildings and urban areas are simplified down into basic shapes with all the fine details neglected and this is either modelled at a smaller scale in a wind tunnel or by using CFD (computer fluid dynamics). However, how do we know whether these models are representative of the real-world?

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This is Straw city, which was built in Silsoe U.K during 2014. You can just see the car behind the array (purple circle), these cubes of straw are 6 m tall, or roughly the height of an average house. Straw city is the stepping stone between the scale models and the real world, and was an urban experiment in a rural environment. We measured inside the array, outside of the array and within the blue building so we could see the link between internal and external flow: which meant the use of drones and smoke machines! The focus of the experiment was on the link between ventilation and the external conditions.

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Smoke releases, drone flying, thermal imaging and tracer gas release: some of the more fun aspects of the fieldwork

After 6 months of data collection, we took the straw cubes away and just monitored the blue cube on its own and the effect of the array can clearly be seen in this plot, where pink is the array, and blue is the isolated cube. So this is showing the pressure coefficient (Cp),  and can be thought of as a way of comparing one building to another in completely different conditions. You can see that the wind direction has an effect and that the array reduces the pressure felt by the cube by 60-90 %. Pressure is linked to the natural ventilation of a building: less pressure means less flow through the opening.

 

Alongside the big straw city, we also went to the Enflo lab at the University of Surrey to run some wind tunnel experiments of our own, which allowed us to expand the array.

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Photos of the wind tunnel arrays. Left is the biggest array modelled, centre is the Silsoe array, top right is the wind tunnel and roughness elements. Bottom right is the model of the storage shed at the full-scale site and centre is the logging system used.

So we have a data set that encompasses all wind directions and speeds, all atmospheric stabilities, different temperature differences and different weather conditions. It’s a big data set and will take a while to work through, especially with comparisons to the wind tunnel model and CFD model created by the University of Leeds. We will also compare the results to the existing guidelines out there and to other similar data sets.

I could ramble on for hours about the work, having spent far too long in a muddy field in all weathers but for more information please email me or come along to my departmental seminar on the 8th November.

This PhD project is jointly funded by the University of Reading and the EPSRC and is part of the Refresh project: www.refresh-project.org.uk