Aerosol Characterization over Penang, Malaysia Using Aerosol Robotic Network (AERONET)

Alias, A. N.; Jafri, Mohd Zubir Mat; Lim, H. S.; Saleh, N. Mohd.; Chumiran, Siti Husniah; Mohamad, A.

doi:10.4028/www.scientific.net/amm.661.81

Cited by 1 publication

(1 citation statement)

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“…Air pollution has become a serious environmental problem in the developing southeast Asian countries, especially in Malaysia that is aiming to become an industrial nation by 2020. Malaysia is ranked as the 117th worst country among 180 nations worldwide in terms of air quality (EPI, 2016) with the major sources of air pollutants to be open biomass burning, motor vehicles and industries (Afroz et al, 2003;Hyer et al, 2013;Alias et al, 2014;Khan et al, 2016a). Besides the local sources, biomass-burning aerosols from wildfires in Indonesia are also transported over Malaysia particularly during the dry season and southwest monsoon (Kanniah et al, 2014a;Khan et al, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

Zaman

Kanniah

Kaskaoutis

2017

Atmospheric Research

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The insufficient number of ground-based stations for measuring Particulate Matter < 10 μm (PM 10) in the developing countries hinders PM 10 monitoring at a regional scale. The present study aims to develop empirical models for PM 10 estimation from space over Malaysia using aerosol optical depth (AOD 550) and meteorological (surface temperature, relative humidity and atmospheric stability) data (retrieved or estimated) from Moderate Resolution Imaging Spectroradiometer (MODIS) during the period 2007-2011. The MODIS retrievals are found to be satisfactorily correlated with ground-based measurements at Malaysia. Multiple linear regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models for PM 10 estimation. The model development and training are performed via comparison with measured PM 10 at 29 stations over Malaysia and reveal that the ANN provides slightly higher accuracy with R 2 = 0.71 and RMSE = 11.61 μg m − 3 compared to the MLR method (R 2 = 0.66 and RMSE = 12.39 μg m − 3). Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD 550 is the most important parameter for PM 10 estimations (R 2 = 0.59 and RMSE = 13.61 μg m − 3); however, the inclusion of the meteorological parameters in the MLR increases the accuracy of the retrievals (R 2 = 0.66, RMSE = 12.39 μg m − 3). The estimated PM 10 concentrations are finally validated against surface measurements at 16 stations resulting in similar performance from the ANN model (R 2 = 0.58, RMSE = 10.16 μg m − 3) and MLR technique (R 2 = 0.56, RMSE = 10.58 μg m − 3). The significant accuracy that has been attained in PM 10 estimations from space allows us to assess the pollution levels in Malaysia and map the PM 10 distribution at large spatial and temporal scales.

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