Multiple regression method to determine aerosol optical depth in atmospheric column in Penang, Malaysia

Abstract

Abstract. Aerosol optical depth (AOD) from AERONET data has a very fine resolution but air pollution index (API), visibility and relative humidity from the ground truth measurements are coarse. To obtain the local AOD in the atmosphere, the relationship between these three parameters was determined using multiple regression analysis. The data of southwest monsoon period (August to September, 2012) taken in Penang, Malaysia, was used to establish a quantitative relationship in which the AOD is modeled as a function of API, relative humidity, and visibility. The highest correlated model was used to predict AOD values during southwest monsoon period. When aerosol is not uniformly distributed in the atmosphere then the predicted AOD can be highly deviated from the measured values. Therefore these deviated data can be removed by comparing between the predicted AOD values and the actual AERONET data which help to investigate whether the non uniform source of the aerosol is from the ground surface or from higher altitude level. This model can accurately predict AOD if only the aerosol is uniformly distributed in the atmosphere. However, further study is needed to determine this model is suitable to use for AOD predicting not only in Penang, but also other state in Malaysia or even global.

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

“…Threshold values in the scatter plot for aerosol classification have been previously reported by Smirnov et al (2002bSmirnov et al ( , 2003, Pace et al (2006), Kaskaotis et al (2007), Toledano et al (2007), Salinas et al (2009), andJalal et al (2012). The data selection criteria proposed by Tan et al (2014a) are used in this study. Seasonal back-trajectory frequency plots from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYS-PLIT_4) model are used to identify the frequency occurrence of origin sources for aerosol and transported pathways (Draxler and Hess, 1998;Wai et al, 2014).…”

“…The present work was based on previous studies of Tan et al (2014a, b). They predicted AOD using multiple regression analysis based on meteorological and air quality data.…”

“…However, the following issues require reconciliation: (i) under-and overprediction of AOD were not assessed because of the lack of available LIDAR data to monitor the variations in the vertical profile of the aerosol distribution, (ii) the algorithm was insufficiently robust because only a 4-month data set was considered; and (iii) seasonal changes other than southwest monsoon were not included in their study. The present study uses a 2-year data set (2012,2013) at Penang to validate the algorithms proposed by Tan et al (2014a, b).…”

“…where RH is the surface relative humidity (Tan et al, 2014a). The root mean square error (RMSE), coefficient of determination (R 2 ), and weighted mean absolute percentage error (wMAPE) between the measured and predicted AOD for each seasonal model were calculated at 95 % confidence level.…”