Although a vast number of air quality investigations have been conducted on the
Mpumalanga Highveld previously, there has been limited attempt to integrate
available datasets from the different methods of air quality monitoring (satellite, insitu
and ground-based observations) and modelling. This study compares modelled,
satellite and measured data to determine the most accurate estimate of ground level
sulphur dioxide (SO2) and nitrogen oxide (NOx) concentrations.
The main value of the project comes from the ‘improvement’ of modelled
concentration fields using measurements. Measurements only provide information on
air quality at isolated places (for example monitoring stations) or at isolated times
(aircraft measurements and satellite observations). Dispersion models predict
concentrations continually over a wide area. However, models have inherent
inaccuracies based on the assumptions made in developing the model and the
variability in the input parameters supplied. These can be accounted for or are part of
the inherent variability of the model results. This study assists in the refinement of
modelled outputs as well as the verification of satellite data using ground-based
measured data as a point of reference.
In the wake of increasing governance on air pollution, industry has been compelled to
account for their impacts on the environment. This study aids industry by proposing a
method to quantify their impacts on the environment and possibly on human health.
Three datasets from 2003 (modelled, measured and satellite) were integrated using a
geographic information system in order to analyse and interrogate the data and
produce an integrated set of data, maps of potentially sensitive ecosystems and maps
of potential exposure to poor air quality of sensitive population groupings.
The results of the study have shown that although the concentration value for the NO2
iv
tropospheric column is greater than the values observed on the ground there is a good
correlation between measured observations and SCIAMACHY retrievals. The sample
size was too small to indicate a statistically significant bias.
The results from the validation of the CALPUFF model indicate with respect to SO2
predictions that themodel is only reliable for 62% of the time within the United States
Environmental Protection Agency’s model performance guideline of acceptance i.e.
predictions within a factor of 2, and for NO2 the predictions are reliable for only 50%
of the time. There is also no constant value by which the model under or over
predicts. The cumulative distribution function graphs illustrate that the CALPUFF
model predicts the highest values from the bulk of the distribution rather that the tail
of the distribution where the extremes lie. This could possibly account for the large
variance between measured and modelled outputs.
The results of identifying areas of potential harm from SO2 emissions reveal that
hotspots for high to very high risk to human health occur around all power stations.
Generally the category of high risk around power stations seems to be located in areas
with population agglomerations between 0-1 000 per km2 and 2001- 5000 per km2.
Several high risk areas for potential harm to ecosystems from SO2 emissions can be
seen on the Highveld with a large spatial extent around Kendal, Matla and Kriel
power station. Approximately 871 wetlands fall within the high risk areas. The
vegetation risk map indicates a high risk to several grassland and bushveld types.
Model results for this study indicate no potential risk to human health from emissions
of NO and subsequent conversion to NO2 in the atmosphere.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/13679 |
| Date | 06 February 2014 |
| Creators | Bhugwandin, Kubeshnie Naicker |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf, application/pdf |
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