Development of representative air quality indicators with measures of uncertainty (MSc / PhD)

Supervisors: Duncan Lee, Marian Scott OBE
Relevant research groups: Environmental Statistics

The air we breathe typically contains a complex mixture of different pollutants, including carbon monoxide, nitrogen dioxide, ozone, particulate matter, and sulphur dioxide. The concentrations of these pollutants are continuously measured at a number of locations throughout the United Kingdom, and these data are used for various purposes. The first is to monitor the UK’s compliance with both domestic and international air quality legislation, such as the UK air quality strategy in 2007, and the European directive 2008/50/EC on ambient air quality in 2008. The second is to keep the public informed of the current and predicted future air quality, so that in the event of high pollution levels, the public can take appropriate action to protect their health. Information on air quality levels is provided to the public via the UK air quality archive, which is available on-line at http://www.airquality.co.uk. However, numerous pollutants are simultaneously measured at multiple monitoring sites, so simplifying summaries (called air quality indicators) are presented that measure overall air quality for a large geographical region on a daily basis. Summaries are presented for the current air quality as well as forecasts for the next day, and are provided for large regions of the UK, such as Greater London and central Scotland. Overall air quality is presented on a 10-point scale, where 1 represents very good air quality, while 10 represents very poor quality.

The aim of this project is to use statistical methods to expand and develop these air quality indicators, so that they are more informative to the general public and policy makers alike. The project will deal with the dual problems of estimating overall air quality, as well as quantitative modeling of uncertainty and risk. Specifically, the studentship will focus on addressing the following three issues.

1. Firstly, the studentship will construct air quality indicators (both current levels and forecasts) for smaller geographical areas than at present, which will provide more local information on the current and predicted air quality levels.
2. At present the current summaries and forecasts take the form of a single estimate (a number between 1 and 10), and do not provide an associated measure of uncertainty. Therefore the second aim of the studentship is to develop methods for quantifying the level of uncertainty in air quality indicators, and develop a mechanism for transmitting this to the public in an informative way.
3. The third focus of the project is to consider how to adjust the air quality indicators in light of the fact that the locations of the pollution monitors are not chosen at random. Instead, a proportion of the monitoring locations are likely to be positioned where pollution levels are highest, which will consequently affect the estimated air quality.

Modelling the evolution of disease risk in space and time (MSc / PhD)

Supervisors: Duncan Lee
Relevant research groups: Biostatistics and Statistical Genetics, Environmental Statistics

Mapping the spatial pattern in disease risk over a city or country is a common problem in epidemiology, and the primary aim is to determine which areas exhibit the greatest risks of disease. A recent extension to this field is to try and model how and to what extent the spatial risk surface changes over time. The motivation for this is to address questions such as: (1) on average across the study region, is the risk of disease getting more or less pronounced? and (2) in which areas of the study region are the disease risks getting worse? This project will develop statistical models to address these questions, and will apply them to map the evolution of important diseases, such as cancer and coronary heart disease, across regions of the UK.

Hearing the full symphony: advancing our understanding of the carbon cycle through continuous monitoring of dissolved organic ca (PhD)

Supervisors: Jonathan Cooper (Engineering), Susan Waldron (GES), Marian Scott OBE
Relevant research groups: Environmental Statistics

This project will, through the development of a sensor to measure the concentration of organic carbon dissolved in water, reveal information on the export of carbon from terrestrial landscapes to aquatic environments. At present, this understanding, generally constructed from grab samples, lacks detail. Dependent on sampling frequency, important periods of carbon export may remain undetected. Capturing detail, through high frequency in-situ measurement, will inform the calculation of accurate budgets of dissolved organic carbon export and provide insight into how the environment controls loss of carbon from the landscape. Such insights will offer societal and scientific advances in many ways, ranging from calculating how long for a windfarm takes to payback carbon expended in construction (construction may impact on terrestrial storage of carbon, later reflected in aquatic export), to incorporating rigourous models of how much carbon is transferred by rivers from land to sea, into global carbon cycle models. Accompanying development of the sensor to quantify DOC export will be contemporaneous characterisation of DOC composition by application of Raman spectroscopy. Should this approach prove viable, if time permits (ultimately dependent on student progress) the doctoral training programme will also explore development of field deployable sensors for logging of in-situ Raman spectra.

On the short-term timescale (of this studentship), the research programme proposed will further our understanding of several NERC fundamental research priorities, namely, "What are the sources, sinks and transportation processes of C within the earth system?", "How is the carbon cycle influenced and integrated by other major biogeochemical cycles?", and thus is appropriate for NERC's key strategic and scientific priorities. However as the research programme will require training and expertise development across disciplines (biogeochemistry, statistical modelling and electronics), and this will be intellectually challenging, the longer term return from such NERC training investment is an intellectually flexible individual, positioned to address future emerging, inter-disciplinary research challenges. Indeed this doctoral training programme develops six of the fifteen key skills recently indentified as critical skill gaps for the environment sector (ERFF Report Number, Most Wanted Skill Needs in the Environment Sector), namely expertise in modelling (of time series), data management, numeracy, multi-disciplinarity, fieldwork and freshwater science.

Spatiotemporal modelling of hydrological catchments (PhD)

Supervisors: Claire Miller (née Ferguson), Marian Scott OBE, Adrian Bowman
Relevant research groups: Environmental Statistics, Statistical Modelling

Regulatory bodies, such as the Environment Agency of England and Wales and the Scottish Environment Protection Agency, regularly monitor river surface water. The purpose of this is to assess the levels of nutrients etc. in the water to report levels to Europe and also to address how to reduce such levels, if necessary, to meet European standards.

River monitoring locations are contained in small waterbodies, which are the standard surface water reporting units for the Water Framework Directive (WFD, European Parliament; 2000). A collection of waterbodies, covering a river network, are contained in a large hydrological area (LHA), and each LHA contains an independent river network. This project will develop spatiotemporal hierarchical models for such data incorporating different levels of spatial correlation within and between catchments and contributory catchment information.

There are several statistical challenges associated with such modelling including incorporating space-time interactions and space-time covariance structures. The overall data dimensionality for such problems is large. However, at particular monitoring locations data can be sparse in space and/or time. Monitoring locations along rivers are flow-connected with models requiring catchment information and river network covariance structures.

Estimating the effects of air pollution on human health (MSc / PhD)

Supervisors: Duncan Lee
Relevant research groups: Biostatistics and Statistical Genetics, Environmental Statistics

The health impact of exposure to air pollution is thought to reduce average life expectancy by six months, with an estimated equivalent health cost of 19 billion each year (from DEFRA). These effects have been estimated using statistical models, which quantify the impact on human health of exposure in both the short and the long term. However, the estimation of such effects is challenging, because individual level measures of health and pollution exposure are not available. Therefore, the majority of studies are conducted at the population level, and the resulting inference can only be made about the effects of pollution on overall population health. However, the data used in such studies are spatially misaligned, as the health data relate to extended areas such as cities or electoral wards, while the pollution concentrations are measured at individual locations. Furthermore, pollution monitors are typically located where concentrations are thought to be highest, known as preferential sampling, which is likely to result in overly high measurements being recorded. This project aims to develop statistical methodology to address these problems, and thus provide a less biased estimate of the effects of pollution on health than are currently produced.

Network design for space weather monitoring (PhD)

Supervisors: Peter Craigmile
Relevant research groups: Environmental Statistics, Statistical Modelling

A PhD opportunity for working on the statistical modelling and design of a sensor network for space weather monitoring is available as part of a new interdisciplinary project on space weather monitoring.

Space weather is the name given to electromagnetic disturbances in the solar system and the near-Earth environment. It can damage spacecraft and disrupt electrical power networks and GPS. The project offered will focus on developing statistical designs of a sensor network of small satellites moving through space, and measuring space weather conditions, including the magnetic field, semi-continuously.

The project is suitable for a student with a strong background in statistics or mathematics, and an interest in space science and data analysis. Candidates should have an excellent single or combined degree in mathematics, statistics or physics and have strong computational skills (for example, using the R language). Excellent written and oral communication skills, and strong time-management skills are also desirable.

The College of Science and Engineering at the University of Glasgow is investing in interdisciplinary research on sensors and sensor system. This exciting project will be co-supervised by academics from the Schools of Physics and Astronomy, Mathematics and Statistics, and Engineering.

The stipend is based on the UK Research Council rates and the studentship will be of 3.5 years duration.

For further information, please contact Dr. Peter Craigmile (peter.craigmile@glasgow.ac.uk)

Deadline for applications: 1 May 2013.
Start date: 1 October 2013.

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