Identification of airborne pollutant sources and estimation of source contributions to air quality ‘hot spots’ are very important in ambient air quality management. Social, economical, political and legal constraints on air quality management demand a convenient and accurate method for attributing air pollution sources to the ‘hot spots’. In this PhD research degree project, an automatic air pollution monitoring station was set up on the library roof at the University of Abertay Dundee to monitor urban background air quality in Dundee. Concentrations of the particulate with aerodynamic diameter less than 10 pm (PMio), the total suspended particulate (TSP), nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx) as well as wind speed, wind direction, ambient temperature and total rainfall were measured continuously for one year. The chemical components of PM10 and TSP, calcium (Ca2+), magnesium (Mg2+), copper (Cu), lead (Pb), nickel (Ni), zinc (Zn), sulphate (SO42'), nitrate (NO3'), chloride (C1‘), ammonium (NH/), sodium (Na+) and potassium (K+) were analysed in the laboratory. Additionally, the inventories of atmospheric emission sources in Dundee were investigated in detail in order to satisfy the needs of air dispersion model. A new software package for the atmospheric dispersion models was also developed by the author using Microsoft Visual C++. In contrast to other available software packages, this package offers a choice of different atmospheric models. The user may select a model according to the situation prevailing and the available parameters. The package for the atmospheric dispersion models was used to simulate transport of airborne pollutants in Dundee. Performance of the models was evaluated using the data gathered at the monitoring station and atmospheric emission inventories. The contributions of various air pollution sources of NOx and PM10 measured at the station were estimated. The receptor model was used to discriminate airborne pollutant emission sources and quantitatively apportion PM10 measured at the station to these sources. The results from the atmospheric dispersion model and the receptor model were compared and used in a complementary manner. A new methodology that combines the features of the receptor oriented and source oriented models, and supplements and corrects the two modelling approaches has been developed. The applicability of the methodology has been tested against the gathered air quality and source emission data in Dundee. The following outputs from the research work are completely novel: • A comprehensive database that consists of concentrations of gaseous pollutants and particulates, chemical compositions of particulates, weather conditions and atmospheric emission inventories. • A new software package for modelling atmospheric dispersion. This was programmed using Microsoft Visual C++. In contrast to other available commercial packages, the models embedded in the package include a modified hybrid plume dispersion model and a ground level release dispersion model that incorporate recent advances in the understanding of planetary boundary layer and atmospheric dispersion. They also include the conventional Gaussian plume dispersion model that is still generally used. The user may select different models according to the prevailing situation and the available parameters when applying the package. • The use of error estimate in ‘weighting’ the data of element matrix and complementary use of subjective information in receptor model trials. • A new methodology that complements atmospheric dispersion and receptor models to attribute sources of airborne pollutants to their receptors.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:247302 |
| Date | January 2002 |
| Creators | Qin, Youjun |
| Contributors | Oduyemi, Kehinde |
| Publisher | Abertay University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://rke.abertay.ac.uk/en/studentTheses/2d3b0ef6-1e78-4b99-80b4-a3bd763db3ce |
Page generated in 0.002 seconds