Urban Meteorology

Tiger Teams: Using Machine Learning to Improve Urban Heat Wave Predictions

The Social MetworkLeave a comment

Adam Gainford – a.gainford@pgr.reading.ac.uk

Brian Lo – brian.lo@pgr.reading.ac.uk

Flynn Ames – f.ames@pgr.reading.ac.uk

Hannah Croad – h.croad@pgr.reading.ac.uk  

Ieuan Higgs  – i.higgs@pgr.reading.ac.uk

What is Tiger Teams?  

You may have heard the term Tiger Teams mentioned around the department by some PhD students, in a SCENARIO DTP weekly update email or even in the department’s pantomime. But what exactly is a tiger team? It is believed the term was coined in a 1964 Aerospace Reliability and Maintainability Conference paper to describe “a team of undomesticated and uninhibited technical specialists, selected for their experience, energy, and imagination, and assigned to track down relentlessly every possible source of failure in a spacecraft subsystem or simulation”.  

This sounds like a perfect team activity for a group of PhD students, although our project had less to do with hunting for flaws in spacecraft subsystems or simulations. Translating the original definition of a tiger team into the SCENARIO DTP activity, “Tiger Teams” is an opportunity for teams of PhD students to apply our skills to real-world challenges supplied by industrial partners.   

The project culminated in a visit to the Met Office to present our work.

Why did we sign up to Tiger Teams?  

In addition to a convincing pitch by our SCENARIO director, we thought that collaborating on a project in an unfamiliar area would be a great way to learn new skills from each other. The cross pollination of ideas and methods would not just be beneficial for our project, it may even help us with our individual PhD work.  

More generally, Tiger Teams was an opportunity to do something slightly different connected to research. Brainstorming ideas together for a specific real-life problem, maintaining a code repository as a group and giving team presentations were not the average experiences one could have as a PhD student. Even when, by chance, we get to collaborate with others, is it ever that different to our PhD? The sight of the same problems …. in the same area of work …everyday …. for months on end, can certainly get tiring. Dedicating one day per week on an unrelated, short-term project which will be completed within a few months helps to break the monotony of the mid-stage PhD blues. This is also much more indicative of how research is conducted in industry, where problems are solved collaboratively, and researchers with different talents are involved in multiple projects at once.

What did we do in this round’s Tiger Teams?  

One project was offered for this round of Tiger Teams: “Crowdsourced Data for Machine Learning Prediction of Urban Heat Wave Temperatures”. The bones of this project started during a machine learning hackathon at the Met Office and was later turned into a Tiger Teams proposal. Essentially, this project aimed to develop a machine learning model which would use amateur observations from the Met Offices Weather Observation Website (WOW), combined with landcover data, to fine-tune model outputs onto higher resolution grids.   

Having various backgrounds from environmental science, meteorology, physics and computer science, we were well equipped to carry out tasks formulated to predict urban heat wave temperatures. Some of the main components included:  

  • Quality control of data – as well as being more spatially dense, amateur observation stations are also more unreliable  
  • Feature selection – which inputs should we select to develop our ML models  
  • Error estimation and visualisation – How do we best assess and visualise the model performance  
  • Spatial predictions – Developing the tools to turn numerical weather prediction model outputs and high resolution landcover data into spatial temperature maps.  

Our supervisor for the project, Lewis Blunn, also provided many of the core ingredients to get this project to work, from retrieving and processing NWP data for our models, to developing a novel method for quantifying upstream land cover to be included in our machine learning models. 

An example of the spatial maps which our ML models can generate. Some key features of London are clearly visible, including the Thames and both Heathrow runways.

What were the deliverables?  

For most projects in industry, the team agrees with the customer (the industrial partner) on end-products to be produced before the conclusion of the project. Our two main deliverables were to (i) develop machine learning models that would predict urban heatwave temperatures across London and (ii) a presentation on our findings at the Met Office headquarters.  

By the end of the project, we had achieved both deliverables. Not only was our seminar at the Met Office attended by more than 120 staff, we also exchanged ideas with scientists from the Informatics Lab and briefly toured around the Met Office HQ and its operational centre. The models we developed as a team are in a shared Git repository, although we admit that we could still add a little more documentation for future development.  

As a bonus deliverable, our supervisor (and us) are consolidating our findings into a publishable paper. This is certainly a good deal considering our team effort in the past few months. Stay tuned for results from our paper perhaps in a future blog post!  

Relationships in errors between meteorological forecasts and air quality forecasts

KajaLeave a comment

Email: K.M.Milczewska@pgr.reading.ac.uk

Exposure to pollutants in the air we breathe may trigger respiratory problems. Pollutants such as ozone (O_{3}) and particulate matter (PM_{2.5}) – particles of about 1/20th of the width of a hair strand – can get into our lungs and cause inflammation, alter their function, or otherwise cause trouble for the cardiovascular system – especially in people with existing underlying respiratory conditions. Although high pollution episodes in the UK are infrequent, the public becomes aware of the associated problems during events such as red skies, in part caused by long-range transport of Saharan dust. Furthermore, the World Health Organisation (WHO) estimates that 85% of UK towns regularly exceed the safe annual PM_{2.5} limit. It is therefore important to forecast surface pollution concentrations accurately in order to enable the public to mitigate some of those adverse health risks.

Figure 1: Smog in London (December 1952). This 5-day event caused many deaths attributable to elevated concentrations of pollutants. The Clean Air Act of 1956 followed. Credit: TopFoto / The Image Works.

In general, air pollution can be difficult to forecast near the surface because of the multitude of factors which affect it. Incorrectly modelling chemical processes within the atmosphere, surface emissions or indeed the meteorology can lead to errors in predicting ground-level pollution concentrations. It is well accepted within the literature that weather forecasting is of decisive importance for air quality. Thus, my PhD project tries to link forecast errors in meteorological processes within the atmospheric boundary layer (BL) with forecast errors in pollutants such as O_{3} and NO_{2} (nitrogen dioxide) using the operational air quality forecasting model in the UK, the Air Quality in the Unified Model (AQUM). This model produces an hourly air quality forecast issued to the public by DEFRA in the form of a Daily Air Quality Index (DAQI) and is verified against surface-based observations from the Automatic Urban and Rural Network (AURN).

Figure 2: Automatic Urban and Rural Network (AURN) ground-based measuring sites for O_{3} and NO_{2}.

A three-month evaluation of hourly forecasts from AQUM shows a delay in the average increase of the morning O_{3} + NO_{2} (‘total oxidant’) concentrations when compared to AURN observations. We also know that BL depth is important for the mixing of pollutants – it acts as a sort of lid on top of the lower part of the troposphere. Since the noted lag in total oxidant increase in our model occurs exactly at the time of the morning BL development, we can form a testable hypothesis: that an inaccurate representation of BL processes – specifically, morning BL growth – leads to a delay in entrainment of O_{3}-rich air masses from the layer of air above it: the residual layer. It has been suggested in the literature that when the daytime convective mixed layer collapses upon sunset, the remaining pollutants are effectively trapped in the leftover (‘residual’) layer, and thus can act as a night-time reservoir of O_{3} above the stable or neutral night-time boundary layer (NBL).

Figure 3: Total oxidant (O_{3} + NO_{2}) average forecast (AQUM, red) and observations (AURN, black) diurnal cycle, averaged over JJA 2017 at 48 urban background sites. Shading is inter-quartile range.
Figure 4: Rate of change of the mean diurnal profile of the forecast (AQUM, red) and observations (AURN, black) of the total oxidant.

To test the hypothesis, semi-idealised experiments are conducted. We simulate a one-month long release of chemically inert tracers within the Numerical Atmospheric Dispersion Environment (NAME) using different sets of numerical weather prediction (NWP) outputs. This enables a process-based evaluation of how different meteorology affects tracers within the BL. Tracers are released within the lateral boundaries of the domain centred on the UK. The idea is to separate the effects of meteorology from chemistry on the tracer concentrations. In particular, we want to understand the role of entrainment of O_{3}-rich air masses from the residual layer down into the developing BL during the morning hours.

We located around 50 AURN sites in urban locations and compared hourly BL depths from June 2017 in the two sets of NWP output used for the tracer simulations: the UKV and UM Global (UMG) configurations of the Met Office Unified Model. It was found that although the average diurnal profiles of BL depth were quite similar, there was a lag in the morning increase of BL depth within the UMG configuration. This may be because the representation of surface sensible heat flux (SSHF) differs in the two NWP models: the UMG uses a single tile scheme to represent urban areas, whereas the UKV uses a more realistic, two-tile scheme (‘MORUSES’) which distinguishes between roof surfaces and street canyons. SSHF is a measure of energy exchange at the ground, where positive fluxes represent a loss of heat from the surface to the atmosphere. Therefore, a more realistic representation of SSHF results in the UKV being better at capturing and storing urban heat. This leads to a faster development of the BL depth in the UKV compared to the UMG, which in turn could mean that there is more turbulent motion and mixing within the atmosphere.

Assuming that the vertical gradient in pollutant concentrations is positive between the morning BL and the free troposphere, mixing air from above should enhance pollutant concentrations nearer to the surface. Our tracer results show that during days when synoptic conditions are dominated by high pressure, the diurnal cycle in forecast and observed surface pollutant concentrations can be adequately replicated by our simplified set-up. Differences between the diurnal cycle between tracer simulations with the two different meteorological set-ups show that the UKV is not only entraining more tracer from above the boundary layer than the simulation using UMG, but also the concentrations increase on average 1 – 2 hours earlier in the morning. These results suggest that indeed the model meteorology – in particular, representation of BL processes – is important to entrainment of polluted air masses into the BL, which in turn has a significant influence on the surface pollutant concentrations.

Within the past two decades, it has been recognised by the weather and air quality modelling communities that neither type of model can truly exist without the other. This post has discussed just one aspect of how meteorology influences the air quality forecast – there are, of course, many other parameters (e.g. wind speed, precipitation, relative humidity) which affect the forecast pollutant concentrations. We therefore also evaluated night-time errors in the wind speed and found that these errors are positively correlated with the total oxidant forecast errors. This means that when the wind speed forecast is overestimated, it is likely to affect the night-time and morning forecast of both O_{3} and NO_{2} in a significant way.

References

Ambient Air Pollution: A global assessment of exposure and burden of disease. WHO, 2016.

Bohnenstengel S., Evans S., Clark P., Belcher S.: Simulations of the London urban heat island, Quarterly Journal of the Royal Meteorological Society, 2011 vol: 137 (659) pp: 1625-1640

Cocks A., 1993: The Chemistry and Deposition of Nitrogen Species in the Troposphere, The Royal Society of Chemistry, Cambridge 1993

Savage N., Agnew P., Davis L., Ordonez C., Thorpe R., Johnson C., O’Connor F., Dalvi M.: Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation, Geoscientific Model Development, 2013 vol: 6 pp: 353-372

Sun J., Mahrt L., Banta R., Pichugina Y.: Turbulence Regimes and Turbulence Intermittency in the Stable Boundary Layer during CASES-99, Journal of the Atmospheric Sciences, 2012 vol: 69 (1) pp: 338-351

Zhang, 2008: Online-coupled meteorology and chemistry models: History, current status, and outlook. Atmos. Chem. Phys, 2008 vol: 8 (11) pp: 2895-2932

Evaluating aerosol forecasts in London

Elliott Warren1 Comment

Email: e.l.warren@pgr.reading.ac.uk

Aerosols in urban areas can greatly impact visibility, radiation budgets and our health (Chen et al., 2015). Aerosols make up the liquid and solid particles in the air that, alongside noxious gases like nitrogen dioxide, are the pollution in cities that we often hear about on the news – breaking safety limits in cities across the globe from London to Beijing. Air quality researchers try to monitor and predict aerosols, to inform local councils so they can plan and reduce local emissions.

Figure 1: Smog over London (Evening Standard, 2016).

Recently, large numbers of LiDARs (Light Detection and Ranging) have been deployed across Europe, and elsewhere – in part to observe aerosols. They effectively shoot beams of light into the atmosphere, which reflect off atmospheric constituents like aerosols. From each beam, many measurements of reflectance are taken very quickly over time – and as light travels further with more time, an entire profile of reflectance can be constructed. As the penetration of light into the atmosphere decreases with distance, the reflected light is usually commonly called attenuated backscatter (β). In urban areas, measurements away from the surface like these are sorely needed (Barlow, 2014), so these instruments could be extremely useful. When it comes to predicting aerosols, numerical weather prediction (NWP) models are increasingly being considered as an option. However, the models themselves are very computationally expensive to run so they tend to only have a simple representation of aerosol. For example, for explicitly resolved aerosol, the Met Office UKV model (1.5 km) just has a dry mass of aerosol [kg kg-1] (Clark et al., 2008). That’s all. It gets transported around by the model dynamics, but any other aerosol characteristics, from size to number, need to be parameterised from the mass, to limit computation costs. However, how do we know if the estimates of aerosol from the model are actually correct? A direct comparison between NWP aerosol and β is not possible because fundamentally, they are different variables – so to bridge the gap, a forward operator is needed.

In my PhD I helped develop such a forward operator (aerFO, Warren et al., 2018). It’s a model that takes aerosol mass (and relative humidity) from NWP model output, and estimates what the attenuated backscatter would be as a result (βm). From this, βm could be directly compared to βo and the NWP aerosol output evaluated (e.g. see if the aerosol is too high or low). The aerFO was also made to be computationally cheap and flexible, so if you had more information than just the mass, the aerFO would be able to use it!

Among the aerFO’s several uses (Warren et al., 2018, n.d.), was the evaluation of NWP model output. Figure 2 shows the aerFO in action with a comparison between βm and observed attenuated backscatter (βo) measured at 905 nm from a ceilometer (a type of LiDAR) on 14th April 2015 at Marylebone Road in London. βm was far too high in the morning on this day. We found that the original scheme the UKV used to parameterise the urban surface effects in London was leading to a persistent cold bias in the morning. The cold bias would lead to a high relative humidity, so consequently the aerFO condensed more water than necessary, onto the aerosol particles as a result, causing them to swell up too much. As a result, bigger particles mean bigger βm and an overestimation. Not only was the relative humidity too high, the boundary layer in the NWP model was developing too late in the day as well. Normally, when the surface warms up enough, convection starts, which acts to mix aerosol up in the boundary layer and dilute it near the surface. However, the cold bias delayed this boundary layer development, so the aerosol concentration near the surface remained high for too long. More mass led to the aerFO parameterising larger sizes and total numbers of particles, so overestimated βm. This cold bias effect was reflected across several cases using the old scheme but was notably smaller for cases using a newer urban surface scheme called MORUSES (Met Office – Reading Urban Surface Exchange Scheme). One of the main aims for MORUSES was to improve the representation of energy transfer in urban areas, and at least to us it seemed like it was doing a better job!

Figure 2: Vertical profiles of attenuated backscatter [m−1 sr−1] (log scale) that are (a, g) observed (βo) with estimated mixing layer height (red crosses, Kotthaus and Grimmond,2018) and (b, h) forward modelled (βm) using the aerFO (section 2).(c, i) Attenuated backscatter difference (βm – βo) calculated using the hourly βm vertical profile and the vertical profile of βo nearest in time; (d, j) aerosol mass mixing ratio (m) [μg kg−1]; (e, k) relative humidity (RH) [%] and (f, l) air temperature (T) [°C] at MR on 14th April 2015.

References

Barlow, J.F., 2014. Progress in observing and modelling the urban boundary layer. Urban Clim. 10, 216–240. https://doi.org/10.1016/j.uclim.2014.03.011

Chen, C.H., Chan, C.C., Chen, B.Y., Cheng, T.J., Leon Guo, Y., 2015. Effects of particulate air pollution and ozone on lung function in non-asthmatic children. Environ. Res. 137, 40–48. https://doi.org/10.1016/j.envres.2014.11.021

Clark, P.A., Harcourt, S.A., Macpherson, B., Mathison, C.T., Cusack, S., Naylor, M., 2008. Prediction of visibility and aerosol within the operational Met Office Unified Model. I: Model formulation and variational assimilation. Q. J. R. Meteorol. Soc. 134, 1801–1816. https://doi.org/10.1002/qj.318

Warren, E., Charlton-Perez, C., Kotthaus, S., Lean, H., Ballard, S., Hopkin, E., Grimmond, S., 2018. Evaluation of forward-modelled attenuated backscatter using an urban ceilometer network in London under clear-sky conditions. Atmos. Environ. 191, 532–547. https://doi.org/10.1016/j.atmosenv.2018.04.045

Warren, E., Charlton-Perez, C., Kotthaus, S., Marenco, F., Ryder, C., Johnson, B., Lean, H., Ballard, S., Grimmond, S., n.d. Observed aerosol characteristics to improve forward-modelled attenuated backscatter. Atmos. Environ. Submitted


AMS Annual Meeting 2019

Lewis BlunnLeave a comment

Email: l.p.blunn@pgr.reading.ac.uk

Between 6th-10th January 2019 I was fortunate enough to attend the 99th American Meteorological Society (AMS) Annual Meeting in their centennial year. It was hosted in the Phoenix, Arizona Convention Center – its vast size was a necessity, seeing as there were 2300 oral presentations and 1100 poster presentations given in 460 sessions! The conferences and symposia covered a wide range of topics such as space weather, hydrology, atmospheric chemistry, climate, meteorological observations and instrumentation, tropical cyclones, monsoons and mesoscale meteorology.

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Me outside one half of Phoenix Convention Center.

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1500 people at the awards banquet.

The theme of this year’s meeting was “Understanding and Building Resilience to Extreme Events by Being Interdisciplinary, International, and Inclusive”. The cost of extreme events has been shown by reinsurance companies to have increased monotonically, with estimated costs for 2017 of $306 billion and 350 lives in the US. Marcia McNutt, President of the National Academy of Science (NAS), gave a town hall talk on the continued importance of evidence-based science in society (view recording). She says that NAS must become more agile at giving advice since the timescales of, for example, hurricanes and poor air quality episodes are very short, but the problems are very complex. There is reason for optimism though, as the new director of the White House Office of Science and Technology Policy is Kelvin Droegemeier, a meteorologist who formerly served as Vice President for Research at the University of Oklahoma.

“Building Resilience to Extreme Events” took on another meaning with the federal shutdown and proved to be the main talking point of this year’s annual meeting. Over 500 people from federally funded organisations such as NOAA could not attend. David Goldston, director of the MIT Washington Office, gave a talk at the presidential forum entitled “Building Resilience to Extreme Political Weather: Advice for Unpredictable Times” (view recording). He made the analogy of both current US political attitude towards climate change and the federal shutdown as being ‘weather’, and thought that politics would return to long-term ‘climate’. He advised scientists to present their facts in a way understandable to public and government, prepare policy proposals, and be clear on why they are not biased. He reassured scientists by saying they have outstanding public support with 76% of the public thinking scientists act in their best interest. During the talk questions were sourced from the audience and could be voted on. The frustration of US scientists with the government was evidently large.

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Questions put forward by the audience and associated votes during Goldston’s talk.

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Ross Herbert (a PDRA in the Reading Meteorology Department) letting his feelings on the federal shutdown be known at the University of Oklahoma after-party.

A growing area of research is artificial and computational intelligence which had its own dedicated conference. As an early career researcher in urban and boundary layer meteorology I was interested to see a talk on “Surface Layer Flux Machine Learning Parametrisations”. By obtaining training data from observational towers it may be possible to improve upon Monin-Obukhov similarity theory in heterogeneous conditions. At the atmospheric chemistry and aerosol keynote talk by Zhanqing Li I learnt that anthropogenic emissions of aerosol can cause a feedback leading to elevated concentration of pollutants. Aerosol reduces solar radiation reaching the surface leading to less turbulence and therefore lower boundary layer height. It also causes warming at the top of the boundary layer creating a stronger capping inversion which inhibits ventilation. Anthropogenic aerosols are not just important for air quality. They affect global warming via their influence on the radiation budget and can lead to more extreme weather through enhancing deep convection.

I particularly enjoyed the poster sessions since they enabled networking with many scientists working in my area. On the first day I bumped into several Reading meteorology undergraduates on their year long exchange at the University of Oklahoma. Like me, I think they were amazed by the scale of the conference and the number of opportunities available as a meteorologist. The exhibition had over 100 organisations showcasing a wide range of products, publications and services. Anemoment (producers of lightweight, compact 3D ultrasonic anemometers) and the University of Oklahoma had stalls showing how instruments attached to drones can be used to profile the boundary layer. This has numerous possible applications such as air quality monitoring and analysing boundary layer dynamics.

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The exhibition (left is a Lockheed-Martin satellite)

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3rd year Reading meteorology undergraduates at the poster session.

Overall, I found the conference very motivating since it reinforced the sense that I have a fantastic opportunity to contribute to an exciting and important area of science. Next year’s annual meeting is the hundredth and will be held in Boston.

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

Christoph KentLeave a comment

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.

Kaja1 Comment

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.

Hannah GoughLeave a comment

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