| Author | Modak, Prasad M. |
| Call Number | AIT Diss. no. EV-84-1 |
| Subject(s) | Air quality management
|
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering, School of Environment, Resources and Development |
| Publisher | Asian Institute of Technology |
| Abstract | Past studies on the optimization of Air Quality Monitoring
Networks (AQMN) have neglected the issues of multiple objectives
and multiple pollutants. These issues are very basic and important
for any practical AQMN design. This research therefore addresses
to this problem and contributes several original methodologies and
formulations.
In this research, an algorithm based on the Minimum Spanning
Tree (MST) is developed to find a joint solution to the problem of
optimum monitor density and monitor configuration. The primary
interest of the MST algorithm is to represent the regionwide air
quality patterns at a minimum of an overlap. As an extension of
this method, a procedure for the 'safe' AQMN design has been
developed, to account for the uncertainties in the simulated air
quality concentrations. Further, a concept of probabilistic
optimization of AQMN has also been introduced .
Two approaches have been developed for the incorporation of
multiple objectives. The first makes use of the utility function
(U approach) and other incorporates on the principles of
sequential interactive compromise (S approach). These algorithms,
which are based on the ideas of the MST, essentially improve the
interests of the associated objectives (such as compliance and/or
estimation of pollution dose to population) by compromising on
the reliability of, pattern representation.
Two approaches have been developed for the incorporation of
multiple pollutants, namely the index-oriented approach and the
pareto optimal method . Since, both the index and the pareto
optimal approaches are essntially the extensions of S and U
methods, a simultaneous consideration of multiple objectives and
multiple pollutants is possible.
As an illustration to the proposed methodologies, a
motivational example of Taipei City, Taiwan has been considered.
Air quality simulations for the pollutants of interest are
necessary for the optimization algorithms developed in this
research . These simulations should be ideally done with the help
of idealized air quality diffusion models, based on the
information on the emissions and the meteorological parameters.
Since this information was not available for the Taipei City case,
a new method, which uses the data at the existing monitoring
network was developed. This method, called ' vector weighing
function method ' is another important contribution in this
research. |
| Year | 1984 |
| Type | Dissertation |
| School | School of Environment, Resources, and Development |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Environmental Engineering and Management (EV) |
| Chairperson(s) | Lohani, B.N. |
| Examination Committee(s) | I, Fude ;Huynh, Ngoc Phien ;Hoshi, K. ;Rossano, Emeritus A.T. |
| Scholarship Donor(s) | The Government of Japan |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 1984 |