Details of Award
NERC Reference : NE/N012704/1
Applying conditional analysis methods to manage fugitive air pollution from regulated industry sites.
Fellowship Award
- Fellow:
- Miss A Sayegh, University of Leeds, Institute for Transport Studies
- Grant held at:
- University of Leeds, Institute for Transport Studies
- Science Area:
- Atmospheric
- Earth
- Overall Classification:
- Unknown
- ENRIs:
- Environmental Risks and Hazards
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Atmospheric Kinetics
- Boundary Layer Meteorology
- Tropospheric Processes
- Regulated industries
- Law and economics
- Pollution
- Air pollution
- Diffuse pollution
- Ferruginous discharge
- Gas emissions
- Nitrates
- Pollutant pathways
- Pollutant transport
- Urban emissions
- Abstract:
- EA regulates industrial sites located within England and Wales which have the potential to emit significant amounts of harmful air pollutants. EA realises that measurement and data analysis techniques are now available to quantify emissions from certain types of industrial processes such as those emitted from point sources (stacks, chimneys, etc.) which help in better regulating and controlling them. However, it is also recognized that other types of emissions, referred to as fugitive emissions, are yet to be as well understood. Speranza (1) identified example fugitive emissions as those originating from a large number of very small, diverse locations around a building, a piece of equipment, a dusty road, or a storage pile. Recent DEFRA (2) report highlights the high uncertainty in the estimates of fugitive emissions of particulate matter, for instance, citing both measurement difficulties and their high dependence on topographical and meteorological conditions which are themselves highly variable in space and time. This work is intended to contribute towards reducing the uncertainties associated with the quantification, attribution, and management of fugitive releases from industrial sites. This project is divided into three main stages. The first stage is to review existing literature on industrial emissions, fugitive emissions, and existing measurement and evaluation methods previously applied to fugitive emissions. The second stage is dedicated to analysing existing data and developing an appropriate framework to applying the conditional statistical analysis method to air quality data surrounding industrial sites. In applying the conditional analysis method, it is also essential to study the level of uncertainty of the assumptions and outputs associated with each step. Therefore, the third stage of this project involves investigating how conditional analysis and uncertainty analysis can be integrated to reduce the uncertainty in the final quantification of fugitive emission source. The combination of the second and third stages could also provide a basis for routinely optimising the outputs of conditional analysis and investigating the impact of more subjective model input parameters. My MSc thesis project involved analysing and modelling environmental time series data using a classification and regression trees technique. This has allowed me to review environmental modelling techniques and understand the complex underlying processes linking emissions at the source to air quality level measured at the receptor and to understand how challenging it can be to account for the different meteorological and background variables involved in the atmospheric dispersion process. In the last 3 years, I have developed working experience of 'R statistical and computing' software and have used it to analyse and model environmental data. I am also currently using it to develop software to analyse and model time-series traffic and emissions data. Throughout my PhD as well, I have been interested in studying how uncertainty in the model inputs and model parameters has an influence on models outputs using for instance randomisation and sensitivity analysis methods. Combining EA/LEC researchers' experience of industrial processing/regulations and environmental analysis techniques with my existing knowledge in environmental data analysis, uncertainty analysis, and 'R' programming can help develop my knowledge in the field and accomplish the main stages of this project. In addition, I would greatly appreciate the opportunity to engage more fully in the 'data analysis to decision making' process, something I think this project, with its strong focus on data analysis to support EA fugitive emissions management activities, could provide. (1) Speranza, P.A. (1993). US Patent No. 5,206,818. Washington, DC: US Patent &Trademark Office. (2) DEFRA. (2014). Air Quality Pollutant Inventories, for England, Scotland, Wales & Northern Ireland.
- NERC Reference:
- NE/N012704/1
- Grant Stage:
- Completed
- Scheme:
- Knowledge Exchange Fellowships
- Grant Status:
- Closed
- Programme:
- Innovation Internships
This fellowship award has a total value of £8,576
FDAB - Financial Details (Award breakdown by headings)
| Exception - Staff | Exception - T&S |
|---|---|
| £6,809 | £1,767 |
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