Effects of land surface temperature on PM10 local variations using [Landsat 8-TIRS] and [GWR approach] / Norziana Ab.llah
Air pollution is one the environmental issues that brings global warming. The main factors that lead to the deterioration of air quality are the development and industrial growth and increasing energy consumption. The conventional regression approach such as Ordinary Lease Square (OLS) is not pos...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/22760/1/TD_NORZIANA%20AB.LLAH%20AP%20R%2019.5.PDF |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Air pollution is one the environmental issues that brings global warming. The main
factors that lead to the deterioration of air quality are the development and industrial
growth and increasing energy consumption. The conventional regression approach such
as Ordinary Lease Square (OLS) is not possible to represent the effects of temperature
to pollutant parameter at local level. The aim of this study is to determine the effects of
land surface temperature (LST) on PMIO local variations using Landsat 8- Thermal
Infrared Sensor (TIRS) and Geographically Weighted Regression (GWR) approach in
2015 at Penang. The objectives of this study are to generate land surface temperature
of study area, to extract value of LST and PMIO on virtual stations, and to determine
the relationship between LST and PMIO. The concentrations of PMIO were taken based
on CAQM stations at Universiti Sains Malaysia (USM), and two stations at Prai while
land surface temperature was derived from a thermal band of Landsat 8-TIRS. By using
kiiging interpolation on virtual stations, there were 565 locations utilized to extract the
values of LST and PMIO. The strong relationship between LST and PMIO is found
based on the local regression analysis of the GWR, (r^=0.606). The result also improved
the global regression analysis (r^= 0.026) in the relationship between both variables. In
the local variation of the relationship between LST and PMIO, there were 70 locations
foxind statistically significant. In conclusion, the regression between satellite remote
sensing and local regression approach such as Landsat 8-TIRS and GWR is possible to
be used to determine the effect of land surface temperature towards air pollution. |
|---|