Determination of Monthly Aerosol Types in Manila Observatory and Notre Dame of Marbel University from Aerosol Robotic Network (AERONET) measurements.

This study aims to identify aerosol types in Manila Observatory (MO) and Notre Dame of Marbel University (NDMU) using Aerosol Robotic Network (AERONET) Level 2.0 inversion data and five dimensional specified clustering and Mahalanobis classification. The parameters used are the 440-870 nm extinction Angström exponent (EAE), 440 nm single scattering albedo (SSA), 440-870 nm absorption Angström exponent (AAE), 440 nm real and imaginary refractive indices. Specified clustering makes use of AERONET data from 7 sites to define 7 aerosol classes: mineral dust (MD), polluted dust (PD), urban industrial (UI), urban industrial developing (UID), biomass burning white smoke (BBW), biomass burning dark smoke (BBD), and marine aerosols. This is similar to the classes used by Russell et al, 2014. A data point is classified into a class based on the closest 5-dimensional Mahalanobis distance (Russell et al, 2014 & Hamill et al, 2016). This method is applied to all 173 MO data points from January 2009 to June 2015 and to all 24 NDMU data points from December 2009 to July 2015 to look at monthly and seasonal variations of aerosol types. The MO and NDMU aerosols are predominantly PD ( 77%) and PD & UID ( 75%) respectively (Figs.1a-b); PD is predominant in the months of February to May in MO and February to March in NDMU. PD results from less strict emission and environmental regulations (Catrall 2005). Average SSA values in MO is comparable to the mean SSA for PD ( 0.89). This can be attributed to presence of high absorbing aerosol types, e.g., carbon which is a product of transportation emissions. The second most dominant aerosol type in MO is UID ( 15%), in NDMU it is BBW ( 25%). In Manila, the high sources of PD and UID (fine particles) is generally from vehicular combustion (Oanh, et al 2006). The detection of BBW in MO from April to May can be attributed to the fires which are common in these dry months. In NDMU, BBW source is from biomass burning (smoldering). In this analysis, smoke from biomass burning transported from other Southeast Asian countries are not observed because of low number of inversion data points. However, fine mode AOD values in NDMU from September to October can have values greater than 1 which implies detection of this transported biomass burning smoke.