Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

• Published in: Atmospheric Chemistry and Physics
• Main Authors: F. Tan, H. S. Lim, K. Abdullah, T. L. Yoon, B. Holben
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2015
• Subjects: Physics; QC1-999; Chemistry; QD1-999; Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/acp-15-3755-2015; https://doaj.org/article/db0547d0eb444027b6be5cc1ad4d0d45

Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloud-cover problem. With the main motivation of overcoming this problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET due to frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The calibrated model coefficients have a coefficient of determination, R 2 , of 0.72. The predicted AOD of the model was generated based on these calibrated coefficients and compared against the measured data through standard statistical tests, yielding a R 2 of 0.68 as validation accuracy. The error in weighted mean absolute percentage error (wMAPE) was less than 0.40% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Performance of our model was compared against selected LIDAR data to yield good correspondence. The predicted AOD can enhance measured short- and long-term AOD and provide supplementary information for climatological studies and monitoring aerosol variation.