Dispersion of PM₁₀ from industrial and road transportation network emissions in the Klang Valley, Malaysia
Particulate matter (PM₁₀) has been the major concern due to their negative impact to the environment, human health and spatial planning for greener environment. In order to have better understanding on the issues, three main objectives were stated in this study. Firstly, was to determine the ai...
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Format: | Thesis |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | http://psasir.upm.edu.my/id/eprint/84448/1/FPAS%202019%208%20-%20ir.pdf http://psasir.upm.edu.my/id/eprint/84448/ |
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Summary: | Particulate matter (PM₁₀) has been the major concern due to their negative impact to the
environment, human health and spatial planning for greener environment. In order
to have better understanding on the issues, three main objectives were stated in this study.
Firstly, was to determine the airborne PM₁₀ emission inventory from the local sources. Secondly,
was to correlate the meteorological conditions resulted from meteorological modelling
towards the study area. Lastly, to validate the obtained concentration thematic map from
the integration modelling approach. The Department of Environment (DOE) reported that almost half
of the total PM₁₀ emission load in Malaysia were contributed by the vehicular and industrial
activities. Therefore, the initiative was taken decades ago to monitor the ambient air
quality with proper recording. However, Malaysia still lack of pollutant emission
inventory due to limitation on expertise. Therefore, this study initiatively collects the
information to execute the PM₁₀ emission inventory from the best available resources. In general,
the air pollutants dispersed freely without knowing the direction and magnitude of the
pollutants. Therefore, the Regional Air Quality Model (RAQM) was used to correlate the calculated
emission with the meteorological conditions forming the PM₁₀ concentration thematic
map. Thus, this modelling approach could address the unmonitored area between the DOE
monitoring stations with providing the PM₁₀ concentration information. The preliminary study
on the localised air quality status was conducted by the descriptive statistical and ANOVA
analysis. Then, the modelling part were initiated with the calculation of the PM₁₀ emission from
two main sources consist of the industrial and road transportation network emission. In the end
producing the emission inventory file to fulfil the first objective. This emission
inventory was processed and converted into gridded emission profile by the
application of the Sparse Matrix Operator Kernal Emission (SMOKE) model. To achieve the
second objective, the gridded meteorological profile was produced from the Fifth Generation
Mesoscale (MM5) model. Community Multiscale Air Quality (CMAQ) model as the chemical transport
modelling system was able to simulate the PM₁₀ concentration thematic map. Thus, the integration process between the models create an
integrated SMOKE-MM5-CMAQ model under similar gridding system
namely known as the RAQM for achieving the third objective. The emission inventory showed higher
contribution of PM₁₀ emissions in industrial source rather than road transportation network.
Whilst, the MM5 model showed positive result in correlating the meteorological conditions. Thus,
the integrated modelling system was able to interpolate the PM₁₀ concentration thematic
map for every location in the domain. However, the obtained concentration was extremely low due
to the limitation on the primary input of the emission. This study only considered the generalised
industrial area basis and the average on-road vehicles’ travel distance emissions from land use map
and vehicles statistic, respectively as the input. Besides, the presence of the
fugitive elements was being underestimated which contributed to the huge
uncertainties in the study. A comprehensive study on determining the fugitive elements in
the future is necessary for the emission input improvement in gaining a
convincing PM₁₀ concentration information. |
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