Dr. Mohd Nadzri Md Reba Profile

Dr. Mohd Nadzri Md Reba

at Univ Teknologi Malaysia

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Proceedings Article | 17 October 2014 Paper

Monitoring particulate matters in urban areas in Malaysia using remote sensing and ground-based measurements

K. Kanniah, Nurul Amalin Fatihah Kamarul Zaman, H. Lim, Mohd Nadzri Md. Reba

Proceedings Volume 9242, 92420J (2014) https://doi.org/10.1117/12.2067029

KEYWORDS: MODIS, Aerosols, Humidity, Data modeling, Atmospheric particles, Atmospheric modeling, Meteorology, Temperature metrology, Remote sensing, Spatial resolution

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Monitoring particulate matter less than 10 μm (PM10) near the ground routinely is critical for Malaysia for emergency management because Malaysia receives considerable amount of pollutants from both local and trans-boundary sources. Nevertheless, aerosol data covering major cities over a large spatial extent and on a continuous manner are limited. Thus, in the present study we aimed to estimate PM10 at 5 km spatial scale using AOD derived from MERIS sensor at 3 metropolitan cities in Malaysia. MERIS level 2 AOD data covering 5 years (2007-2011) were used to develop an empirical model to estimate PM10 at 11 locations covering Klang valley, Penang and Johor Bahru metropolitan cities. This study is different from previous studies conducted in Malaysia because in the current study we estimated PM10 by considering meteorological parameters that affect aerosol properties, including atmospheric stability, surface temperature and relative humidity derived from MODIS data and our product will be at ~5 km spatial scale. Results of this study show that the direct correlation between monthly averaged AOD and PM10 yielded a low and insignificant relationship (R²= 0.04 and RMSE = 7.06μg m^-3). However, when AOD, relative humidity, land surface temperature and k index (atmospheric stability) were combined in a multiple linear regression analysis the correlation coefficient increased to 0.34 and the RMSE decreased to 8.91μg m^-3. Among the variables k- index showed highest correlation with PM 10 (R²=0.35) compared to other variables. We further improved the relationship among PM10 and the independent variables using Artificial Neural Network. Results show that the correlation coefficient of the calibration dataset increased to 0.65 with low RMSE of 6.72μg m^-3. The results may change when we consider more data points covering 10 years (2002- 2011) and enable the construction of a local model to estimate PM10 in urban areas in Malaysia.

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